Introduction to computer vision, Three R's of computer vision, Basics of image processing, Low, Mid & high-level vision, digital image processing, digital video processing, and Machine/Robot vision, Color Fundamentals, Image rectification, Image enhancement, super-resolution,Noise removal techniques, and image transformation.Edge detection, Region of Interest (ROI), image Dilation and Erosion,corner detection image segmentation,image sampling, Image classification, Recognition,hi-pass filter, low-pass filter, Fast Fourier transmission (FFT), Morphological operations, Pattern recognition, Concept of Dimensions,Image Formation on Camera, Concept of Pixel,Perspective Transformation, Types of Images, Color Codes Conversion, Grayscale to RGB Conversion, Concept of Sampling, Pixel Resolution, Spatial Resolution, Concept of Quantization, Concept of Dithering, Histograms Introduction, Brightness and Contrast, Histogram Equalization, Concept of Mask, Concept of Blurring, Concept of Edge Detection, Introduction to the frequency domain, Fourier Series and Transform, Convolution Theorem, High Pass vs Low Pass Filters, Introduction to Color Spaces, Introduction to JPEG Compression, Bag of features and large-scale instance recognition.

  

Different types of UAS (Drone), Different components of UAS, Battery, Motor, ESC and Propeller, Assembly of Drones, Flying Principles, Flight Controller and peripherals, Navigation and Communication Radio Controller Transmitter, Mission Planning and Control Station, Launch and Recovery Equipment, Payloads, Data Links, Government Policies, Ground Support Equipment, Introduction to drone assembly

  

• C Programming:

Introduction to C, History, Standards, Overview of C Basics: Variables, Data Types, Constants, Qualifiers, Operators, Control Structures, Pointers: Concept of pointers Pointer arithmetic, Chain of pointers, Pointer to const, const pointers, Void pointer, NULL pointer, Arrays: 1D, 2D arrays, Pointers & Arrays, Functions: Overview of functions, Scope & Lifetime of variables, Recursion, External linkage, C Preprocessor, Strings, Structures & unions, Dynamic Memory management.

• Python Programming:

Python basics, If, If- else, Nested if-else, looping, for, While, Nested loops, Control Structure, Break, Continue, Pass, Strings and Tuples, Accessing Strings, Basic Operations, String slices, Working with Lists, Accessing list, Operations, Function and Methods, Files, Modules, Dictionaries, Functions and Functional Programming, Declaring and calling Functions, Declare, assign and retrieve values from Lists, Introducing Tuples, Accessing tuples, Visualizing using Matplotlib, Seaborn, OOPs concept, Class and object, Attributes, Inheritance, Overloading, Overriding, Data hiding, Operations Exception, Exception Handling, except clause, Try-finally clause, User Defined Exceptions, Creation of python virtual environment.

   Database Concepts (File System and DBMS), OLAP vs OLTP, Database Storage Structures (Tablespace, Control files, Data files), Structured and Unstructured Data, SQL Commands (DDL, DML & DCL), Conditional Constructs in SQL, Data collection, Designing database schema, Normal Forms and ER Diagram, Relational Database modeling, working with MongoDB, Connecting DB’s with Python, Introduction to Data Driven Decisions, Introduction to Spatial Datasets,Spatial Database, POSTGIS, Spatial objects for POSTGRESQL, Creating spatial indexes, Spatial analysis in SQL, Spatial queries, Spatial joins.

  

Linear Algebra & Calculus: Vectors, scalars, Vector projection, Cosine similarity, Orthogonal vectors, Linear combination, Linear span, Linear independence, Basics of Matrices, Scalar and matrix, Linear transformation, Diagonal, Orthogonal, Ortho-normal, Positive definite matrix, Eigen values & eigen vectors, Concept, Intuition, Significance, How to find principle component analysis, Concept, Properties, Applications, Singular value decomposition, Concept, Properties, Applications, Gradient algorithms, Local/Global maxima and minima, Saddle point, Convex functions, Gradient descent algorithms-batch, Mini-batch, Stochastic, Performance comparison.

Analytics and Visualization: Introduction to Business Analytics using some case studies, Descriptive Statistics and its measures, Probability theory, Probability Distributions (Continuous and discrete- Normal, Binomial and Poisson distribution) and Data, Sampling and Estimation, Statistical Interfaces, Predictive modeling and analysis, Bayes’ Theorem, Central Limit Theorem, Data Exploration & Preparation,Concepts of Correlation, Covariance, Pearson Correlation, Outliers, Regression Analysis, Forecasting Techniques, Simulation and Risk Analysis, Hypothesis Techniques, Z-Test, chi-Square Test, Skewness

Predictive Modeling, Decision Tree concept, Usage of Decision Trees in making predictions, Entropy and Information Gain, Linear Programming Concepts and Example

Decision Analytics: Evaluating Classifiers, Analytical Framework, Evaluation, Baseline. Python Libraries –NumPy, SciPy/ScikitLearn, Pandas, Matplotlib

Basics of Data Visualization: Using Excel and Tableau, Usage of various graphs for appropriate data representation (e.g. pie chart, bar chart, box plots, histogram, gantt chart, line chart, scatter plot), Python examples for each of them

Case Studies: 3D mapping, Crop Management(crop diseases and crop management /Agriculture DRONE Applications), geo accurate maps.

  

ML algorithms, linear and nonlinear classification, regression techniques, support vector machines, KNN, K-means , decision trees, oblique trees, random forest, Bayesian analysis and naive bayes classifier, gradient boosting, ensemble methods, bagging & boosting , association rules learning, Apriori and FP growth algorithms, clustering, overview of factor analysis, ARIMA, ML in real time, algorithm performance metrics, ROC, AOC, confusion matrix, F1 Score, MSE, MAE, DBSCAN clustering in ML, anomaly detection, recommender system.

Overview of reinforcement learning, agent environment framework, Markov decision processes, returns, and value functions, MDP, Bellman's equations, dynamic programming (model based prediction, model based control), model free prediction - simulation based methods (Monte Carlo learning, Temporal difference learning), eligibility traces, lambda method, Model free control (Model free control method), Table lookup case, Value function approximation models and planning, Simulation based methods like Q-learning.

Case Studies related to Drone Technologies using Machine Learning concepts

  

Introduction to Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long short-term memory (LSTM), backpropagation, forward passing/propagation,hyperparameter tuning, loss functions, optimizers, training, validating, testing and inference of a model.

Introduction to TensorFlow, PyTorch and Keras, Building basic deep learning models using Keras with Tensorflow backend.Troubleshooting, and fine tuning deep learning models. Analysis of model performance on tensorboard.

Data preparation and pre-processing, Digital Image Processing – concepts, color fundamentals, Image rectification, image enhancement, super-resolution, image transformation. Region of Interest (ROI), Object Localization, image classification, semantic segmentation, instance segmentation and object detection and recognition which are of object of interest.

Introduction to State-Of-The-Art(SOTA) Models, AlexNet, ImageNet, AlexNet, ResNet, FRCNN, ImageNet, VGGNet, Google’s InceptionNet, YOLO, DeepLab, U-Net etc.

Introduction transfer learning, feature extraction and finetuning of pre-trained models. inductive, unsupervised transductive, deep learning tools & techniques, tuning deep learning models, 

Latest trends, research, applications and case studies of deep learning

  

Microcontroller Fundamentals:

Overview of Microcontrollers, Microprocessors and SoC, RISC vs CISC, Harvard vs Princeton Architectures, Embedded Memories, Timers/Counters, Programming in Embedded C, Blink an LED, Reading Analog Pins and Converting the Input to a Voltage, Blink an LED using Timers, PWM LED Brightness Control, Using Buttons, Debouncing a Button, Serial Communications (UART), DHT11 Interfacing.

Introduction to Drone Wireless Protocols:

OSI Layer, Introduction to IoT, Internet of Everything, Significance of IoT Enabled Drones, Wireless Sensor Components, Overview of BLE, IEEE 802.11 – Wi-Fi, Introduction and standards, GPS, LiDAR, RF Based Drone Protocols.

Introduction to IoT Protocols: Communication models, Request and Response, Pub/Sub, Why IoT Protocols, MQTT-Pub/Sub model, Broker, Topic, Introduction to REST based API, Resources, Methods , GET, PUT, POST and DELETE, IoT Platform integration options with http and MQTT.

  

Aerodynamic Forces and Moments, Design Criteria, Objectives, and Priorities, Feasibility Analysis, Design Process, UAV Conceptual Design, UAV Preliminary Design, UAV Detail Design, Basic Fundamentals of Flight Software, Software Development, Operating System, Management Software, Software Integration, Development and Integration of Payload Software, Setting up the build and simulation environment, MAVProxy, Drone kit and PyMAVLink library, Wireless components relevant with UAS, Drone electronics components and architecture, flight controller, measurement of roll and pitch angle using IMU, communication protocols, drone application, PID control system, complementary filter, Electronics Speed Controllers, Power Distribution Board, Advanced Drone Assembly- Initialization, calibration, and configuration of flight controller using GCS, SITL, Flight Testing, Post flight data analysis.

  

Remote sensing : Introduction to remote sensing, electromagnetic radiation, remote sensing types, data acquisition & platforms, image resolutions, FCC, image interpretation; GIS – introduction to GIS, vector and raster data models, map projections, open GIS data and national geospatial data policy; Global Positioning Systems (GPS) - basic GPS concept, differential GPS (DGPS), applications of GPS; Digital Photogrammetry- introduction to photogrammetry, ortho-photos.

Spatial analysis: statistical analysis, measurement (length, distance, area, shape), spatial and non-spatial query, proximity analysis (buffering), overlay analysis, multi-criteria analysis, change detection, spatial modeling.

GIS Development :Introduction to map API, Web GIS and services, desktop GIS customization, customization of quantum GIS using python, Case studies – applications of GIS.

  

Introduction to containers, Introduction to DevOps, Introduction to Containers, Advantages of using container based applications, Installing docker and using basic docker commands, Build your own container based application image, Networking in Docker, Managing containers – Logs / Resources, Introduction to Kubernetes, Need for Kubernetes, Introduction to Kubernetes cluster, Management node, Worker Nodes, Pods, Deployment, Service Types etc.Working with Kubernetes Cluster.

Introduction to Version control systems, Creating GitHub repository, Using Git – Introduction to git commands.

Cloud Computing Basics, Understanding Cloud Vendors, Definition, Characteristics, Components, Cloud provider, SAAS, PAAS, IAAS and other Organizational scenarios of clouds, Administering & Monitoring cloud services, benefits, and limitations, Deploy application over AWS cloud. Comparison among SAAS, PAAS, IAAS, Cloud Products and Solutions, Cloud Pricing, Compute Products and Services, Elastic Cloud Compute, Dashboard.

   Percentage, Profit & Loss, Ratio & Proportion, Average, Mixture & Allegation, Simple Interest & Compound Interest, Number Systems , Series, Cyclicity & Remainders, Data Interpretation, Syllogism, Coding & Decoding, Blood Relations, Seating Arrangements (Linear & Circular), Ages, Puzzles, Time, Speed & Distance, Trains, Boats & Streams, Time & Work, Wages (Man days), Pipes & Cisterns, Clocks, Permutations & Combinations, Probability, Calendar Fundamentals of Communication, The Art of Communication, Personality Development, English Grammar, Correct Usage of English, Common Mistakes in English Communication, Listening Skills, Reading Skills, Writing Skills, Public Speaking, Presentation Skills, Group Discussions, Interpersonal Skills, Personal Interviews

   Students are required to execute project work for the duration of four weeks (after the completion of all modules) as a part of this course. For seminar, students need to choose the topic themselves and give the seminar on the respective dates allocated by the concerned faculty members. The topic chosen by the students should be relevant to the Embedded Systems Design. Project work is distributed in the following phases:

1. Study and Requirements Elicitation
2. Design
3. Implementation
4. Testing
5. Project report
6. Viva Voce and Presentation

Students need to submit a project report at the conclusion of the project. Mentors should be allocated within 3 weeks of the course commencement and should be executed throughout the course duration. The students should maintain a logbook, which contains their day-to-day activities during the project phases. The mentor allocated for that project should sign this logbook regularly. The allocated 4 weeks should be focused on implementation, testing and consolidating the documentation.