PG Diploma in Unmanned Aircraft System Programming (PG-DUASP)
The theoretical and practical mix of the Post Graduate Diploma in Unmanned Aircraft System Programming (PG-DUASP) course has the following focus:
- Understanding UAS/Drone and its allied technologies:
- Various types of Unmanned Aircraft Systems and the DGCA guidelines for drone flying in India
- Hands on the various sensors, IoT tools etc.
- Principles for UAS/ Drone Design & Prototyping
- Knowledge of Geographic Information Systems, Data Analytics, and Visualization for UAS/ Drone based application
- Real time applications of UAS/ Drone related to surveying & mapping, infrastructure inspection, healthcare, agriculture etc.
- Graduate in Engineering or Technology (10+2+4 or 10+3+3 years) in any discipline of Engineering, OR
- M.Sc/MS (10+2+3+2 years) in Computer Science, IT, Electronics. OR
- MCA, MCM, OR
- Post Graduate Degree in Physics / Mathematics / Statistics, OR
- Post Graduate Degree in Management with graduation in IT / Computer Science / Computer Applications
- The candidate must have 55% in the qualifying degree.
PG-DUASP course will be delivered in fully PHYSICAL mode. The total course fee and payment details are as detailed herein below:
The total course fee is INR. 90,000/- plus Goods and Service Tax (GST) as applicable by Government of India (GOI).
The course fee for PG-DUASP has to be paid in two installments as per the schedule.
- First installment is INR. 10,000/- plus Goods and Service Tax (GST) as applicable by GOI.
- Second installment is INR. 80,000/- plus Goods and Service Tax (GST) as applicable by GOI.
The course fee includes expenses towards delivering classes, conducting examinations, final mark-list and certificate, and placement assistance provided.
The first installment course fee of Rs 10,000/- + GST on it as applicable at the time of payment is to be paid online as per the schedule. It can be paid using credit/debit cards through the payment gateway. The first installment of the course fees is to be paid after seat is allocated during counseling rounds.
The second installment of the course fees is to be paid before the course commencement through NEFT.
NOTE: Candidates may take note that no Demand Draft (DD) or cheque or cash will be accepted at any C-DAC or NIELIT training centre towards payment of any installment of course fees.
- Java Programming:
Data Types, Operators and Language, Constructs, Inner Classes and Inheritance, Interface and Package, Exceptions, Collections, Threads, Java.Lang, Java.util, Java Virtual Machine, Lambda Expressions, Functional Programming and Interfaces, Introduction to Streams, JDBC API, Reflection
- 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
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
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.
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.
- Study and Requirements Elicitation
- Project report
- 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.
After successfully completing this course, students will be able to:
- Design, plan, and execute UAS missions while adhering to safety guidelines and industry best practices
- Receive data and discover patterns in the user data and make predictions based on these intricate patterns
- Apply knowledge in developing advanced image and video analysis solutions powered by Machine Learning and Artificial Intelligence