My Blogs

Square and Fair: The Role of Square Images in Deep Learning

DEV.to

Why Square Images Dominate Deep Learning and CNNs

medium

Python Implementation: Quantum Algorithm for Maximum Discounts

hashnode

Quantum Algorithm for Coalition Structure Generation: Example and Implementation

medium

Quantum Algorithm Implementation: Finding the Best Deals

DEV.to

Publications

Qubit-efficient Variational Quantum Algorithms for Image Segmentation

Abstract: Quantum computing is expected to transform a range of computational tasks beyond the reach of classical algorithms. In this work, we examine the application of variational quantum algorithms (VQAs) for unsupervised image segmentation to partition images into separate semantic regions. Specifically, we formulate the task as a graph cut optimization problem and employ two established qubit-efficient VQAs, which we refer to as Parametric Gate Encoding (PGE) and Ancilla Basis Encoding (ABE), to find the optimal segmentation mask. In addition, we propose Adaptive Cost Encoding (ACE), a new approach that leverages the same circuit architecture as ABE but adopts a problem-dependent cost function. We benchmark PGE, ABE and ACE on synthetically generated images, focusing on quality and trainability. ACE shows consistently faster convergence in training the parameterized quantum circuits in comparison to PGE and ABE. Furthermore, we provide a theoretical analysis of the scalability of these approaches against the Quantum Approximate Optimization Algorithm (QAOA), showing a significant cutback in the quantum resources, especially in the number of qubits that logarithmically depends on the number of pixels. The results validate the strengths of ACE, while concurrently highlighting its inherent limitations and challenges. This paves way for further research in quantum-enhanced computer vision.

@article{venkatesh2024qubit,
              title={Qubit-efficient Variational Quantum Algorithms for Image Segmentation},
              author={Venkatesh, Supreeth Mysore and Macaluso, Antonio and Nuske, Marlon and Klusch, Matthias and Dengel, Andreas},
              journal={arXiv preprint arXiv:2405.14405},
              year={2024}
            }

Preprint

Q-Seg: Quantum Annealing-based Unsupervised Image Segmentation

Abstract: In this study, we present Q-Seg, a novel unsupervised image segmentation method based on quantum annealing, tailored for existing quantum hardware. We formulate the pixel-wise segmentation problem, which assimilates spectral and spatial information of the image, as a graph-cut optimization task. Our method efficiently leverages the interconnected qubit topology of the D-Wave Advantage device, offering superior scalability over existing quantum approaches and outperforming state-of-the-art classical methods. Our empirical evaluations on synthetic datasets reveal that Q-Seg offers better runtime performance against the classical optimizer Gurobi. Furthermore, we evaluate our method on segmentation of Earth Observation images, an area of application where the amount of labeled data is usually very limited. In this case, Q-Seg demonstrates near-optimal results in flood mapping detection with respect to classical supervised state-of-the-art machine learning methods. Also, Q-Seg provides enhanced segmentation for forest coverage compared to existing annotated masks. Thus, Q-Seg emerges as a viable alternative for real-world applications using available quantum hardware, particularly in scenarios where the lack of labeled data and computational runtime are critical.

@article{venkatesh2023q,
              title={Q-seg: Quantum annealing-based unsupervised image segmentation},
              author={Venkatesh, Supreeth Mysore and Macaluso, Antonio and Nuske, Marlon and Klusch, Matthias and Dengel, Andreas},
              journal={arXiv preprint arXiv:2311.12912},
              year={2023}
            }

Preprint | Code

QuACS: Variational Quantum Algorithm for Coalition Structure Generation in Induced Subgraph Games

Abstract: Coalition Structure Generation (CSG) is an NP-Hard problem in which agents are partitioned into mutually exclusive groups to maximize their social welfare. In this work, we propose QuACS, a novel hybrid quantum classical algorithm for Coalition Structure Generation in Induced Subgraph Games (ISGs). Starting from a coalition structure where all the agents belong to a single coalition, QuACS recursively identifies the optimal partition into two disjoint subsets. This problem is reformulated as a QUBO and then solved using QAOA. Given an n-agent ISG, we show that the proposed algorithm outperforms existing approximate classical solvers with a runtime of \mathcal{O}(n^2) and an expected approximation ratio of 92\%. Furthermore, it requires a significantly lower number of qubits and allows experiments on medium-sized problems compared to existing quantum solutions. To show the effectiveness of QuACS we perform experiments on standard benchmark datasets using quantum simulation.

@inproceedings{10.1145/3587135.3592192,
              author = {Venkatesh, Supreeth Mysore and Macaluso, Antonio and Klusch, Matthias},
              title = {QuACS: Variational Quantum Algorithm for Coalition Structure Generation in Induced Subgraph Games},
              year = {2023},
              isbn = {9798400701405},
              publisher = {Association for Computing Machinery},
              address = {New York, NY, USA},
              url = {https://doi.org/10.1145/3587135.3592192},
              doi = {10.1145/3587135.3592192},
              abstract = {Coalition Structure Generation (CSG) is an NP-Hard problem in which agents are partitioned into mutually exclusive groups to maximize their social welfare. In this work, we propose QuACS, a novel hybrid quantum-classical algorithm for Coalition Structure Generation in Induced Subgraph Games (ISGs). Starting from a coalition structure where all the agents belong to a single coalition, QuACS recursively identifies the optimal partition into two disjoint subsets. This problem is reformulated as a QUBO and then solved using QAOA. Given an n-agent ISG, we show that the proposed algorithm outperforms existing approximate classical solvers with a runtime of O(n2) and an expected approximation ratio of 92\%. Furthermore, it requires a significantly lower number of qubits and allows experiments on medium-sized problems compared to existing quantum solutions. To show the effectiveness of QuACS we perform experiments on standard benchmark datasets using quantum simulation.},
              booktitle = {Proceedings of the 20th ACM International Conference on Computing Frontiers},
              pages = {197–200},
              numpages = {4},
              keywords = {Coalition Game Theory, Quantum AI, Quantum Computing},
              location = {Bologna, Italy},
              series = {CF '23}
              }

Publication | Preprint | Code

GCS-Q: Quantum Graph Coalition Structure Generation

Abstract: The problem of generating an optimal coalition structure for a given coalition game of rational agents is to find a partition that maximizes their social welfare and known to be NP-hard. Though there are algorithmic solutions with high computational complexity available for this combinatorial optimization problem, it is unknown whether quantum-supported solutions may outperform classical algorithms. In this paper, we propose a novel quantum-supported solution for coalition structure generation in Induced Subgraph Games (ISGs). Our hybrid classical-quantum algorithm, called GCS-Q, iteratively splits a given n-agent graph game into two nonempty subsets in order to obtain a coalition structure with a higher coalition value. The GCS-Q solves the optimal split problem O(n) times, exploring $O(2^n)$ partitions at each step. In particular, the optimal split problem is reformulated as a QUBO and executed on a quantum annealer, which is capable of providing the solution in linear time with respect to n. We show that GCS-Q outperforms the currently best classical and quantum solvers for coalition structure generation in ISGs with its runtime in the order of $n^2$ and an expected approximation ratio of 93% on standard benchmark datasets.

@InProceedings{10.1007/978-3-031-36030-5_11,
              author="Venkatesh, Supreeth Mysore
              and Macaluso, Antonio
              and Klusch, Matthias",
              editor="Miky{\v{s}}ka, Ji{\v{r}}{\'i}
              and de Mulatier, Cl{\'e}lia
              and Paszynski, Maciej
              and Krzhizhanovskaya, Valeria V.
              and Dongarra, Jack J.
              and Sloot, Peter M.A.",
              title="GCS-Q: Quantum Graph Coalition Structure Generation",
              booktitle="Computational Science -- ICCS 2023",
              year="2023",
              publisher="Springer Nature Switzerland",
              address="Cham",
              pages="138--152",
              abstract="The problem of generating an optimal coalition structure for a given coalition game of rational agents is to find a partition that maximizes their social welfare and known to be NP-hard. Though there are algorithmic solutions with high computational complexity available for this combinatorial optimization problem, it is unknown whether quantum-supported solutions may outperform classical algorithms.",
              isbn="978-3-031-36030-5"
              }

Publication | Preprint | Code

BILP-Q: Quantum Coalition Structure Generation

Abstract: Quantum AI is an emerging field that uses quantum computing to solve typical complex problems in AI. In this work, we propose BILP-Q, the first-ever general quantum approach for solving the Coalition Structure Generation problem (CSGP), which is notably NP-hard. In particular, we reformulate the CSGP in terms of a Quadratic Binary Combinatorial Optimization (QUBO) problem to leverage existing quantum algorithms (e.g., QAOA) to obtain the best coalition structure. Thus, we perform a comparative analysis in terms of time complexity between the proposed quantum approach and the most popular classical baselines. Furthermore, we consider standard benchmark distributions for coalition values to test the BILP-Q on small-scale experiments using the IBM Qiskit environment. Finally, since QUBO problems can be solved operating with quantum annealing, we run BILP-Q on medium-size problems using a real quantum annealer (D-Wave).

@inproceedings{10.1145/3528416.3530235,
                author = {Venkatesh, Supreeth Mysore and Macaluso, Antonio and Klusch, Matthias},
                title = {BILP-Q: quantum coalition structure generation},
                year = {2022},
                isbn = {9781450393386},
                publisher = {Association for Computing Machinery},
                address = {New York, NY, USA},
                url = {https://doi.org/10.1145/3528416.3530235},
                doi = {10.1145/3528416.3530235},
                abstract = {Quantum AI is an emerging field that uses quantum computing to solve typical complex problems in AI. In this work, we propose BILP-Q, the first-ever general quantum approach for solving the Coalition Structure Generation problem (CSGP), which is notably NP-hard. In particular, we reformulate the CSGP in terms of a Quadratic Binary Combinatorial Optimization (QUBO) problem to leverage existing quantum algorithms (e.g., QAOA) to obtain the best coalition structure. Thus, we perform a comparative analysis in terms of time complexity between the proposed quantum approach and the most popular classical baselines. Furthermore, we consider standard benchmark distributions for coalition values to test the BILP-Q on small-scale experiments using the IBM Qiskit environment. Finally, since QUBO problems can be solved operating with quantum annealing, we run BILP-Q on medium-size problems using a real quantum annealer (D-Wave).},
                booktitle = {Proceedings of the 19th ACM International Conference on Computing Frontiers},
                pages = {189–192},
                numpages = {4},
                keywords = {quantum computing, quantum AI, coalition game theory},
                location = {Turin, Italy},
                series = {CF '22}
                }

Publication | Preprint | Code

A practical approach for road quality estimation using smartphone based inertial data: IMU data processing pipeline to estimate road quality

Abstract: The tight financial situation in many municipalities does not allow them to record and evaluate the condition of their own transport infrastructure in detail. The present rule-based road quality estimation methods are outdated, very expensive and less accurate. The only subjective and non-recurring documentation leads to the fact that there is no resilient data basis for intelligent, data-based condition forecasts, which would actually be possible with methods of artificial intelligence (AI) and machine learning (ML). The considerable potential for cost minimization that such forecasts would open up via maintenance optimization remains untapped. In this research work, we demonstrate a road quality estimation system given the Inertial Measurement Unit (IMU) data from smartphone mounted on a vehicle. The system consists of a data preprocessing pipeline which removes many uncertainties along with more accurate geo-referencing of the raw data, and training a machine learning model to estimate road quality in terms of a continuous variable. Route quality information is gathered together with GPS tracking using the IMU data coming from smartphone mounted on a vehicle. The ground-truth (road quality) is obtained using conventional road quality measurement system. Next, distinctive features are obtained from the IMU raw data. Consequently, a machine learning model is trained to estimate the road quality from the obtained features with high performance.

@inproceedings{10.1145/3529399.3529414,
                author = {Nagaraj, Deepak and Mutz, Marcel and Mysore Venkatesh, Supreeth and Riebschlaeger, Lea and Werth, Dirk},
                title = {A Practical Approach for Road Quality Estimation using Smartphone based Inertial Data: IMU data processing pipeline to estimate road quality},
                year = {2022},
                isbn = {9781450395748},
                publisher = {Association for Computing Machinery},
                address = {New York, NY, USA},
                url = {https://doi.org/10.1145/3529399.3529414},
                doi = {10.1145/3529399.3529414},
                abstract = {The tight financial situation in many municipalities does not allow them to record and evaluate the condition of their own transport infrastructure in detail. The present rule-based road quality estimation methods are outdated, very expensive and less accurate. The only subjective and non-recurring documentation leads to the fact that there is no resilient data basis for intelligent, data-based condition forecasts, which would actually be possible with methods of artificial intelligence (AI) and machine learning (ML). The considerable potential for cost minimization that such forecasts would open up via maintenance optimization remains untapped. In this research work, we demonstrate a road quality estimation system given the Inertial Measurement Unit (IMU) data from smartphone mounted on a vehicle. The system consists of a data preprocessing pipeline which removes many uncertainties along with more accurate geo-referencing of the raw data, and training a machine learning model to estimate road quality in terms of a continuous variable. Route quality information is gathered together with GPS tracking using the IMU data coming from smartphone mounted on a vehicle. The ground-truth (road quality) is obtained using conventional road quality measurement system. Next, distinctive features are obtained from the IMU raw data. Consequently, a machine learning model is trained to estimate the road quality from the obtained features with high performance.},
                booktitle = {Proceedings of the 2022 7th International Conference on Machine Learning Technologies},
                pages = {87–91},
                numpages = {5},
                keywords = {Sensor data processing, Road quality estimation, Predictive maintenance, Feature extraction},
                location = {Rome, Italy},
                series = {ICMLT '22}
                }

Publication Link

Education

RPTU Kaiserslautern-Landau

PhD(c), Computer Science

Jan 2023 - Present Kaiserslautern, Germany

Thesis Title: Quantum Artificial Intelligence for Satellite-based Earth Observation

Saarland University

Master's degree, Mathematics and Computer Science

Mar 2021 - Nov 2022 Saarbruecken, Germany

Courses:

Inverse Problems
Dynamical Systems
Neural Networks: Theory and Implementation
Artificial Intelligence
Digital Signal Processing
Human Computer Interaction
Mathematical Aspects of Quantum Mechanics
Hybrid Learning with Reinforcement Learning
Numerical Laboratory in Computerized Tomography

Thesis: On Quantum Coalition Structure Generation

Skills: Matplotlib, Mathematical Analysis, Process Automation, Computer Vision, Quantum Computing, Time Series Analysis, Neural Networks, Statistical Data Analysis, Software Development, Digital Signal Processing, NumPy, Teamwork, Programming, LaTeX, Artificial Intelligence (AI), Discrete Mathematics, Deep Learning, Python, Jupyter, Test Automation, Pandas, TensorFlow, Natural Language Processing (NLP), Qiskit, Theoretical Computer Science, Machine Learning, C, PyTorch, Analytics, Linux, Git, Image Processing, Algorithms

The National Institute Of Engineering

Bachelor of Engineering (BE), Information Science/Studies

Sep 2013 - Jul 2017 Mysuru, India

Grade: 9.18

Rank: Secured the 5th rank among 126 students

Skills: Matplotlib, Cloud Computing, Java, Leadership, Software Development, Digital Signal Processing, Teamwork, Programming, LaTeX, Artificial Intelligence (AI), SQL, Python, Theoretical Computer Science, C, Software Architecture, Analytics, Linux, Git, Algorithms

Vijaya Vittala Composite Pre University College

Pre University, PCMC(S)

Jun 2011 - May 2013 Mysuru, India

Subjects: Physics, Chemistry, Mathematics, and Computer Science

Skills: Leadership, Teamwork, Programming, SQL, Python, Theoretical Computer Science, C, Linux, Algorithms

Experience

German Research Center for Artificial Intelligence (DFKI)

Researcher

Jan 2023 - Present Saarbruecken, Germany

Analyzing the feasibility of near-term quantum technology for computer vision in the domain of satellite-based Earth observation.
Developing novel quantum algorithms for computationally expensive tasks with practical implications.

German Research Center for Artificial Intelligence (DFKI)

Master Thesis

Mar 2021 - Nov 2022 Saarbruecken, Germany

Thesis: On Quantum Coalition Structure Generation

BILP-Q: First end-to-end quantum solver for the coalition structure generation problem.
GCS-Q: A hybrid gate-based quantum-classical algorithm for coalition structure generation in Induced subgraph games.
QuACS: A quantum gate-based algorithm for finding optimal coalition structure in Induced subgraph games.

Keywords: QC/QIP, QUBO, BILP, QAOA

August-Wilhelm Scheer Institute for digitized products and processes gGmbH

Junior Researcher Machine Learning

May 2019 - Nov 2022 Saarbruecken, Germany

Research project KIKI: Object detection problem for automatic defect detection of sewer pipes using image processing and computer vision techniques. Built a microservice architecture for machine learning model development, inferencing, and data visualization.
Industrial project with Saar Waste Management Association (EVS): Analysis of sensor (time-series) data for anomaly detection in sewer plant machinery.
Research project DatEnKoSt: Coordinate transformation of smartphones' IMU sensors (accelerometer, gyroscope, magnetometer) and time-series analysis using machine learning for road quality prediction.
Research project Make Your Own Wearable (MYOW): NLP-based recommender system for the front end of a website for building clothes with custom requirements.
Research project KAMeri: A Brain Computer Interface system to assess the state of mind of a worker and recommend actions to keep the worker safe at the industrial site.

Saarland University

Teaching Assistant

Mar 2021 - Sep 2021 Saarbruecken, Germany

Tutor for the course Digital Signal Processing offered by the Spoken Language Systems group.

Infineon Technologies

Software Engineer

Sep 2017 - Feb 2019 Bengaluru, India

Process automation, tool development using Python.
Testing of AURIX 1GMCAL software products using proprietary tools like UVP, HyperTerminal, and C compilers like GNU, Tasking and Windriver supporting two AUTOSAR versions AS321 and AS403.
Experience with Jenkins automation framework and IBM's Clearcase for version control.

Motorola Mobility (a Lenovo Company)

Software Intern

Jan 2017 - Jul 2017 Bengaluru, India

Developed a tool to fetch data from Cloud (Google BigQuery) and create local custom views.
The tool was developed from scratch, to speed up the development process, using Python with the stable and structured Django framework. Python provides a vast range of libraries to deal with databases, access the cloud, design web pages, and leverage its robust built-in data structures. Additionally, it offers an object-oriented paradigm which reduces code size with enhanced readability.

The National Institute Of Engineering

Placement Coordinator

Jul 2016 - Jun 2017 Mysuru, India

Shortlisted students' data comprising of their academic records and any additional data on demand was passed on to the recruiters.
Conveyed information from the recruiters and decisions from the placement division at The National Institute of Engineering, Mysuru for the final year undergraduates during the in-campus placement drives.
ISO 9001:2008 certified.

Projects

Q(AI)² : Quantum Artificial Intelligence for the Automotive Industry

Apr 2021 - Mar 2024

Quantum algorithms can qualitatively accelerate the solving of certain hard computing problems. Artificial intelligence and its applications are also among the important key technologies of our time. The Q(AI)² project brings both together using applications within the automotive industry, in particular for optimal, flexible production planning in the context of Industry 4.0, collision-free maneuver planning of autonomous vehicles, capacitive route planning as well as cooperative services and smart mobility. This clear basis of concrete tasks in the automotive industry relevant to the products of the future as the start and end of the research is a key unique selling point of this project.
The aim of the project is to develop a broad basis of algorithms for AI applications with optimizations on quantum computers. To this end, a deeper understanding of the acceleration potential is being developed for the already known algorithms. Furthermore, it is identified in which fundamental and industrially relevant applications quantum AI delivers significant acceleration. The implementation of algorithms is optimized for both the available hardware and the industrial issue. This creates a clear and qualified outlook for the first quantum-accelerated AI applications in the automotive sector.
If the outcome is positive, the results will flow directly into concrete pre-development projects for the companies involved and will also be made available to external users. These can give German automobile manufacturers decisive competitive advantages.
Q(AI)² Publications

Skills: Algorithms · Machine Learning · Artificial Intelligence (AI) · Quantum Computing · Theoretical Computer Science

KIKI

Jan 2022 - Nov 2022

A computer vision project for automatic defect detection of sewer pipes. The sewer network is a central part of the residential and industrial infrastructure in Germany. The sewer network is subject to a natural aging process, which without continuous care and maintenance can lead to functional failure and even - in the case of leaks - to contamination of the groundwater.
The video of a camera passing inside a sewer pipe is annotated with a defect class manually by a domain expert. By building a dataset of annotated frames of the video, modeling it as a multi-label classification problem for automatic defect detection can save significant manual effort, time, and cost.

Skills: Pandas (Software) · Git · Programming · Teamwork · Algorithms · Discrete Mathematics · Agile Methodologies · Software Development · Machine Learning · Deep Learning · Computer Vision · Artificial Intelligence (AI) · Cloud Computing · Time Series Analysis · Software Architecture · Statistical Data Analysis · Neural Networks · Microservices · Linux · Python (Programming Language) · Matplotlib · NumPy · Image Processing · TensorFlow · Analytics · PyTorch · Jupyter · Mathematical Analysis

DatEnKoSt

May 2021 - Mar 2022

A project for detection of road quality using various smart phone sensors. Transformation from phone body coordinates to absolute earth coordinates. Pattern recognition of time series sensor data and classifying the street quality following the Zentrale Kommunale Entsorgungsbetrieb (ZKE) standards.

Skills: Pandas (Software) · Git · Programming · Teamwork · Algorithms · Discrete Mathematics · Software Development · Machine Learning · Deep Learning · LaTeX · Computer Vision · Digital Signal Processing · Artificial Intelligence (AI) · Docker · Cloud Computing · Time Series Analysis · MongoDB · Statistical Data Analysis · Neural Networks · Microservices · Linux · Python (Programming Language) · Matplotlib · NumPy · Image Processing · TensorFlow · Protocol Buffers · Analytics · PyTorch · Jupyter · Mathematical Analysis

Anomaly Detection of Industrial Machines

Apr 2021 - Dec 2021

A machine learning project for detection of anomaly in machines at a sewage plant (Entsorgungs Verband Saar). Collection of Data using sensors from the industrial site. Development of a hybrid supervised and unsupervised model to classification and pattern recognition of sensor time series data.

Skills: Pandas (Software) · Git · Programming · Teamwork · Algorithms · Discrete Mathematics · Software Development · Machine Learning · Deep Learning · Leadership · Digital Signal Processing · Artificial Intelligence (AI) · Cloud Computing · Time Series Analysis · Statistical Data Analysis · Neural Networks · Microservices · Linux · Python (Programming Language) · Matplotlib · Scrum · NumPy · TensorFlow · Protocol Buffers · Analytics · PyTorch · Jupyter · Mathematical Analysis

MYOW (Make Your Own Wearable)

Dec 2019 - May 2021

MYOW is a platform for a user to build accessories/clothes with necessary sensors/actuators for custom requirements. Worked on the recommender system for the User Interface of this web app. Provided the service as an gRPC request/response implemented as a Python app with Clean Architecture (Dependency Injection). Dockerized as a micro service. Use of Cython interpretor for speed up of complex Mathematical operations.
Recommender system business logic implemention using NLP (Natural Language Processing) techniques involving word2vec, keyword extraction and use of Google News pre-trained model as part of feature extraction. Linear Algebraic operations to find similarity scores used to map the recommendations.

Skills: Pandas (Software) · Git · Programming · Teamwork · Algorithms · Discrete Mathematics · Agile Methodologies · Software Development · Machine Learning · Deep Learning · Artificial Intelligence (AI) · Docker · Cloud Computing · Natural Language Processing (NLP) · MongoDB · Software Architecture · Statistical Data Analysis · Neural Networks · Microservices · Linux · Python (Programming Language) · Matplotlib · NumPy · TensorFlow · Protocol Buffers · Analytics · Jupyter · Mathematical Analysis

KAMeri (Cognitive Occupational Safety for Human-Machine Interaction)

May 2019 - Jan 2020

Handle the incoming data of worker's EEG brain signals, learn his mental state and send recommendations to robot associated to that worker.
The entire software framework was built as a micro service architecture using:
- MQTT lens as an EEG headset simulator
- HIVE MQ as an initial broker
- Envoy Proxy as reverse proxy
- RabbitMQ as a message Broker
- Node as a backend service
- Dashboard as an Angular App
All the above services were dockerized as microservices.

Skills: Pandas (Software) · Git · Programming · Teamwork · Algorithms · Discrete Mathematics · Software Development · Digital Signal Processing · Docker · Cloud Computing · MongoDB · Software Architecture · Statistical Data Analysis · Microservices · Linux · Python (Programming Language) · Matplotlib · SQL · Scrum · NumPy · Protocol Buffers

AURIX 1G MCAL

Mar 2018 - Jan 2019

Automation scripts in Python for extraction of Memory Maps in source codes of AURIX 1G MCAL for all modules and report generation.
Python script for AMDC Tool verification (check the uniqueness of UUIDs in a file), SafetyCase extraction from mht files.
Calculating the Worst Case Execution Time for all APIs in Modules like ADC, PWM, CANTRCV, SPI, (using Rapita Tool, batch scripts to sequentially call numerous processes, and perl scripts to modify .mak files and Rapita utils).
Package Testing (using Hyper Terminal or Termite), Crypto Verification (checking for the integrity of bmd and xdm files in EB Tresos) and Warning verification for releases.
PWM Module Test Improvement Tasks (Modifying the test script, NI's .vi files and test case parameters in .uprj files via UVP tool).

Skills: Git · Programming · Teamwork · Process Automation · Jenkins · Jira · C · Scrum

SafeTLib

Sep 2017 - Jan 2019

Developed Signature Tool (a plugin for eclipse for verifying program flow monitoring using seed and signature by calculating consolidated CRC32).
Developed Bit Restore Tool to check registers in the AURIX Microcontroller for integrity after executing each safety module.
The tool prints all the registers of the target device, executes the configured module and again print the registers into an excel sheet, finally creates a report of comparison with allowed exemptions/justifications.
Testing and debugging using Universal Debug Engine (UDE) of Safety products from Infineon for AURIX Automotive Microcontrollers.

Skills: Git · Programming · Teamwork · Leadership · Process Automation · Test Automation · Jira · C · Analytics

SafeTMon and SafeTCore

Sep 2017 - Mar 2018

Execution of all tests to get a hand-on of the tools like Universal Validation Platform (UVP), Universal Debug Engine (UDE), IBM Clearcase versioning and labels, Memtool, Hyperterminal, Teraterm, Termite.
Periodic maintenance tests were executed in UVP.
PRO-SIL SafeTMon Release Notes Addendum updates (change in information of documentation after verifying the tests executed as part of periodic maintenance).
Clarification for issues from customers like WABCO, HITEX etc. (OEMs often get in contact with us to get trivial clarifications like the number of heartbeats per clock tick for a particular configuration or STM Timer value which will be used while developing their applications).
Setup a Legacy Maintenance Museum, a web page and a database consisting information regarding the products in passive maintenance which helps as a quick reference for any engineer to start working on the project in case of urgent customer queries.

Skills: Git · Programming · Teamwork · Leadership · Process Automation · Test Automation · Jira · C

CD MAT Tool

Jan 2017 - Jul 2017

Designed and implemented a tool to analyze data from google cloud.
Worked with an agile team and investigated design approaches very often.
Big Data Analytics Engine: Part of a team that built a predictive and prescriptive analytics on scale using SQL server.
The data from the cloud was fetched into the local machine and filtered based on the requirement. Previously this process was taking a huge time with an excel sheet maintained with complex formulas applied to each cell which was later replicated in python object-oriented scripts and the whole process became just the click of a button.
Frontend was developed using HTML and CSS to view the Database.

Skills: Git · Programming · Teamwork · Algorithms · Agile Methodologies · Software Development · Leadership · Process Automation · Cloud Computing · Test Automation · Linux · Python (Programming Language) · Matplotlib · SQL · Analytics

Location based Wireless Sensor Network

Jan 2017 - May 2017

This project proposed an efficient mechanism to get parameters from sensors using cloud-based IoT with an application. It provided access control only for intended users of the application. Also, the data was accessible only for users requesting the data from a predefined geographical boundary provided during the registration of the user in the android application. Two sensors were used, viz. pressure and methane sensor. The application was developed on an Android platform (mobile device) and the database in the cloud was maintained using SQLyog interface. The data from the sensors were read using the Arduino Microcontroller and uploaded to the cloud database using the Raspberry Pi 3.