About Me |
|---|
Swarna Prabha Jena is working as an Assistant Professor in the Department of Electronics and Communication Engineering at Centurion University of Technology and Management, Bhubaneswar, India. She is a member of different academic bodies like IEEE, ISTE, and the Institution of Engineers. She has published many national and international publications, patents and book chapters. One patent has been granted to her credit. She has also organised many faculty and student development programmes like Embedded Systems, Advanced Embedded Systems, IoT, IIoT, etc. She works in different thrust areas like Edge Computing, Edge AI, Data Science, Computer Vision, Smart Agriculture, IoT, IIoT, and Embedded Systems. She has guided many undergraduate students in these areas and has also been a reviewer for many International Journals and Conferences. Her interest is in working with cutting-edge technologies and research techniques that could bring change and motivate people around us. She is a creative and dedicated professional with an excellent work ethic. She has an experience of working in a high work-pressure environment and multidisciplinary teams. Her ability to multitask and good understanding on cutting-edge technologies for providing a complete solution make an ideal candidate for the organisation.
|
- Embedded System & IOT
- Edge Computing
- Edge ML
- IIoT
- Precision Agriculture
- Internet of Things
- Electronic Devices
- Microprocessor and Microcontroller
- Embedded System Design
- Microcontroller and its Applications
- Digital Electronics Circuits
- Microsystems
- Internet of Things
- IoT Analytics
- Embedded system Design using ARM cortex
- Embedded Linux using Linux
- Real time Operating system using FreeRTOS
- Microcontrollers and its variants
- IoT
- IIoT
- Embedded System Design
- Embedded Image Processing
- Internet of Things
- Machine Learning and Artificial Intelligent
- Edge Computing using Greengrass
- Teaching and Research
Research Fields
Currently, I am focusing on developing a solution for the apparel industry through AWS Greengrass. So that the efficiency of the operators and the machine can be tracked remotely through Edge Computing.
Guiding undergraduate students in completing their course projects in-house.
Recent Publications (International Journals)
| Sl. No. | Title | Issuer |
|---|---|---|
| 1 | Certificate of Merits in Embedded System Certification Test | Manish Kumar Bhojasia Director & Founder Bangalore, India |
| 2 | Certificate for Reviewer | IEEE Bangladesh Section and IEEE Bhubaneswar Section |
| 3 | Jury for Odisha Skill Competition | HN Shashidhara, World skill Member |
| 4 | EMINENT ACHIEVER AWARD | Centurion University of Technology and Management |
Academic Honors & Awards
HDL Implementation and performance comparison of an optimized High Speed multiplier
Implementation of Hand Gesture System for Speech Impaired People
Classroom Attendance System with IoT Notification
Self -Driven Metro and Biometric Reservation System using ARM Microcontroller
Research on the development of a new shop application using android
Computerized Green House Data Acquisition System Using Arduino with LabVIEW
Accelerometer based gesture controlled robot using Arduino
FPGA implementation of Angle Generator for CORDIC Based High pass FIR Filter Design
Voice Based Electronic Guidance System for Visually Impaired Pedestrians
Wireless Temperature Humidity Monitoring Controlling and Analysis of Multiple Room
Smart Sopping Basket: An Application of Radio Frequency Identification Technique
Barcode Detection using Computer Vision
ARM-7 Based Alcohol Detection and Accident Prevention of Vehicle
Remote Monitoring Controlling and Automation of Protected Environment Cultivation
LPG Leakage Detection and Automated Booking System using LPC2129
Real-Time Slide Door Automation and Security System with RF Technology
Study of Iris Flower Classification using Logistic Regression and Principal Component Analysis
Automatic Cell Detection and Segregation in Microscopy Image using Image Processing Toolbox
Road Detection Region Growing by Markov Random Field
Implementation of Hand Gesture System for Speech Impaired People
Uses of Sensor and IoT in Protected Cultivation
Enhancing Healthcare with Edge AI for Analysis of Cough Detection
IoT technology for cough detection is promising in healthcare and public health monitoring. It offers a real-time, non-intrusive, and data-driven methodology for identifying and analyzing coughing events, ultimately improving our capacity to monitor and address respiratory health issues in a connected world. Nowadays, subjective evaluations are frequently used in clinical measurement, although they are neither accurate nor trustworthy. Advancements in sensor technology, machine learning, and data analytics are developing a cost-effective, proper cough detection system that is increasingly practicable and beneficial. This paper applies the Keras library imported to the neural network to the acoustic characteristics model. A method for detecting cough with the help of a Mobile App is made with Arduino Nano 33 BLE Sense and Edge Impulse Studio. The proposed system can discriminate between cough sounds and other undesired noises from extensive training data with an accuracy of 98%.
Integration of Cellular IoT for Greenhouse Monitoring, Controlling and Notification System
To harvest crops at the ideal time, conventional farmers must often physically tour the farms to measure various environmental parameters, including temperature, humidity, light intensity, and soil moisture. Even though this traditional farming technique has been around for a while, farmers occasionally have trouble measuring all environmental conditions effectively, leading to inconsistent productivity rates. A greenhouse system must constantly monitor and control environmental variables such as temperature, humidity, soil moisture, light intensity, etc. for a greenhouse system, are required. This research drives deep into the core of exploring the potential of GSM (Global System for Mobile Communication) technology and embarking on the journey of software to revolutionize greenhouse management. The primary goal of the work is to ensure greenhouse internal parameters are suitable for plants. In this paper, cellular IoT technology like GSM bridges plant growth by developing a remote measurement and control system for the greenhouse. To tackle the challenge, employ methods to monitor temperature, humidity, light intensity, gas and soil moisture by integrating GSM. The data collected by the sensors allows monitoring through an IoT analytics platform and sending SMS to control internal parameters required by the plants. It also presents real-time sensor information with graphical charts. The work contributes to more efficient and sustainable greenhouse practices.
Automatic leaf diseases detection and classification of cucumber leaves using internet of things and machine learning models
Automation of agriculture with the use of cutting-edge technology is a growing research area. It addresses the issue of better yields and tries to mitigate the negative impact due to climatic changes, attacks of diseases, and pests in crops. Hence to overcome the problem of disease attacks, this research proposes an automatic leaf disease detection and classification system using a web app that can help the farmer to identify the occurrence of leaf diseases remotely. Further performance matrices like confusion matrix, overall classification accuracy, precision, sensitivity, specificity, and ROC-AUC score have been calculated to test the efficacy of models. The simulated results proved that the CNN with 10-fold cross-validation has got an accuracy of 99.47% and it significantly outperforms other existing counterparts. The data collected from both environments have been compared and analysed. This study offers a real-time application of the internet of things and machine learning in agriculture.
Classifications on wine informatics using PCA, LDA, and supervised machine learning techniques
Proving the quality of a food product is challenging for any country. Every country recommends using products whose quality has been assured. A similar thing applies to the wine industry. To promote their products, wine industries acquire quality certifications through expert assessments. It is an expensive and time-consuming process. This paper explores the usage of machine algorithms like principle component analysis (PCA), linear discriminant analysis (LDA), random forest (RF), Gaussian naive Bayes (GNB), decision trees (DT), K-nearest neighbour (KNN), logistic regression (LR), and gradient boost (GB) for classifying the wine data into three main categories. The experimental work provides a comparative study of the accuracy of all classifiers is discussed in detail.
Implementing IIoT in Garment Production Line: A Case Study of a Full-sleeve Shirt
In this research paper, a comprehensive look at recent trends and a review of the use of cutting-edge technology and methods such as Internet of Things (IoT), RFID models, as well as specific areas of application and uses in Apparel technology areas are explored. In the manufacturing industry, the production line method is proved as the most effective process. Thus, the processing time has to be ideally planned and executed to achieve the maximum possible efficiency. This study describes monitoring the stitching process of a standard full-sleeve shirt using the latest technology with a cost-effective approach. Modern technologies like IIoT, RFID, MQTT, and Wi-Fi are employed. The study concentrated to track the time consumed by each job per operation for a full-sleeve shirt. In this analysis process, the working of the technology, architecture of the concept, and various devices used were studied in deep and analyzed to make this study a cost-effective and efficient way of upgrading the existing industries to the next level, i.e., Industry 4.0.
In this research paper, a comprehensive look at recent trends and a review of the use of cutting-edge technology and methods such as Internet of Things (IoT), RFID models, as well as specific areas of application and uses in Apparel technology areas are explored. In the manufacturing industry, the production line method is proved as the most effective process. Thus, the processing time has to be ideally planned and executed to achieve the maximum possible efficiency. This study describes monitoring the stitching process of a standard full-sleeve shirt using the latest technology with a cost-effective approach. Modern technologies like IIoT, RFID, MQTT, and Wi-Fi are employed. The study concentrated to track the time consumed by each job per operation for a full-sleeve shirt. In this analysis process, the working of the technology, architecture of the concept, and various devices used were studied in deep and analyzed to make this study a cost-effective and efficient way of upgrading the existing industries to the next level, i.e., Industry 4.0.