Tag: Machine Learning

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The article titled, “Recent Advances in Thermal Imaging and It’s Applications using Machine Learning: A Review,” has been accepted for publication in the IEEE Sensors Journal (2023). 

Wilson A N, Khushi Gupta, Balu Harshavardan Koduru, Abhinav Kumar, Ajit Jha, and Linga Reddy Cenkeramaddi, “Recent Advances in Thermal Imaging and It’s Applications using Machine Learning: A Review,” has been accepted for publication in the IEEE Sensors Journal (2023).

Keywords: Imaging, Cameras, Sensors, Temperature sensors, Optical sensors, Machine learning, Thermal sensing, cameras, data privacy, image colour analysis, image sensors, infrared imaging, learning (artificial intelligence), reviews, machine-learning techniques, RGB imaging, thermal cameras, thermal images, thermal imaging sensor technology, thermal imaging technology, thermal imaging-based applications

Recent Advances in Thermal Imaging and It’s Applications using Machine Learning: A Review

Abstract: Recent advancements in thermal imaging sensor technology have resulted in the use of thermal cameras in a variety of applications, including automotive, industrial, medical, defense and space, agriculture, and other related fields. Thermal imaging, unlike RGB imaging, does not rely on background light, and the technique is nonintrusive while also protecting privacy. This review article focuses on the most recent advancements in thermal imaging technology, key performance parameters, an overview of its applications, and machine-learning techniques applied to thermal images for various tasks. This article begins with the most recent advancements in thermal imaging, followed by a classification of thermal cameras and their key specifications, and finally a review of machine-learning techniques used on thermal images for various applications. This detailed review article is highly useful for designing thermal imaging-based applications using various machine-learning techniques.

More details: DOI: 10.1109/JSEN.2023.3234335

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The article titled, “Generalization of Relative Change in a Centrality Measure to Identify Vital Nodes in Complex Networks,” has been accepted for publication in IEEE Access (2022).  

KODURU HAJARATHAIAH, MURALI KRISHNA ENDURI, SATEESHKRISHNA DHULI, SATISH ANAMALAMUDI1, AND LINGA REDDY CENKERAMADDI, “Generalization of Relative Change in a Centrality Measure to Identify Vital Nodes in Complex Networks,” has been accepted for publication in IEEE Access (2022).  

Keywords: Volume measurement, Time measurement, Time complexity, Object recognition, Machine learning, Laplace equations, Market research

Abstract: Identifying vital nodes is important in disease research, spreading rumors, viral marketing, and drug development. The vital nodes in any network are used to spread information as widely as possible. Centrality measures such as Degree centrality (D), Betweenness centrality (B), Closeness centrality (C), Katz (K), Cluster coefficient (CC), PR (PageRank), LGC (Local and Global Centrality), ISC (Isolating Centrality) centrality measures can be used to effectively quantify vital nodes. The majority of these centrality measures are defined in the literature and are based on a network’s local and/or global structure. However, these measures are time-consuming and inefficient for large-scale networks. Also, these measures cannot study the effect of removal of vital nodes in resource-constrained networks. To address these concerns, we propose the six new centrality measures namely GRACC, LRACC, GRAD, LRAD, GRAK, and LRAK. We develop these measures based on the relative change of the clustering coefficient, degree, and Katz centralities after the removal of a vertex. Next, we compare the proposed centrality measures with D, B, C, CC, K, PR, LGC, and ISC to demonstrate their efficiency and time complexity. We utilize the SIR (Susceptible-Infected-Recovered) and IC (Independent Cascade) models to study the maximum information spread of proposed measures over conventional ones. We perform extensive simulations on large-scale real-world data sets and prove that local centrality measures perform better in some networks than global measures in terms of time complexity and information spread. Further, we also observe the number of cliques drastically improves the efficiency of global centrality measures.

More details: DOI: 10.1109/ACCESS.2022.3232288

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The article titled, “Depth Camera based Dataset of Hand Gestures” has been accepted for publication in the Data in Brief Journal (2022).

Sindhusha Jeeru, Arun Kumar Sivapuram, David Gonzalez Leon, Gröli Jade, Sreenivasa Reddy Yeduri, Linga Reddy Cenkeramaddi, “Depth Camera based Dataset of Hand Gestures,” has been accepted for publication in the Data in Brief Journal (2022).

Keywords: Video hand gestures, RGB image, Depth image, RGB-D Camera, Machine learning

Abstract: The dataset contains RGB and depth version video frames of various hand movements captured with the Intel RealSense Depth Camera D435. The camera has two channels for collecting both RGB and depth frames at the same time. A large dataset is created for accurate classification of hand gestures under complex backgrounds. The dataset is made up of 29718 frames from RGB and depth versions corresponding to various hand gestures from different people collected at different time instances with complex backgrounds. Hand movements corresponding to scroll-right, scroll-left, scroll-up, scroll-down, zoom-in, and zoom-out are included in the data. Each sequence has data of 40 frames, and there is a total of 662 sequences corresponding to each gesture in the dataset. To capture all the variations in the dataset, the hand is oriented in various ways while capturing.

More details: https://doi.org/10.1016/j.dib.2022.108659

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The article, “A Novel Angle Estimation for mmWave FMCW radars using Machine Learning,” has been accepted for publication in the IEEE Sensors Journal.

L. R. Cenkeramaddi, P. K. Rai, A. Dayal, J. Bhatia, A. Pandya, J. Soumya, A. Kumar, & A. Jha., “A Novel Angle Estimation for mmWave FMCW radars using Machine Learning,” IEEE Sensors Journal 2021 (in press).

Keywords: Radar, Radar antennas, Estimation, Azimuth, Chirp, Sensors, Machine learning

Abstract: In this article, we present a novel machine learning based angle estimation and field of view (FoV) enhancement techniques for mmWave FMCW radars operating in the frequency range of 77 – 81 GHz. Field of view is enhanced in both azimuth and elevation. The Elevation FoV enhancement is achieved by keeping the orientation of antenna elements in elevation. In this orientation, radar focuses the beam in vertical direction there by enhancing the elevation FoV. An Azimuth FoV enhancement is achieved by mechanically rotating the radar horizontally, which has antenna elements in the elevation. With the proposed angle estimation technique for such rotating radars, root mean square error (RMSE) of 2.56 degrees is achieved. These proposed techniques will be highly useful for several applications in cost-effective and reliable autonomous systems such as ground station traffic monitoring and control systems for both on ground and aerial vehicles.

More details:DOI: 10.1109/JSEN.2021.3058268

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ACPS Research Group along with the top Indian Institutes lead the Indo-Norwegian collaboration in Autonomous Cyber-Physical Systems (INCAPS) Project

  • 2019 – Indo-Norwegian collaboration in Autonomous Cyber-Physical Systems (INCAPS): The Research Council of Norway (NFR) under the INTPART program; Project Manager and PI: Professor Linga Reddy Cenkeramaddi, ACPS Research Group, Department of ICT, UiA Grimstad, Project duration: 2019-2023. Partners and/or collaborators: Indian Institute of Science, Bangalore (IISc); Indian Institute of Technology, Hyderabad (IITH); International Institute of Information Technology, Hyderabad (IIITH); Birla Institute of Technology and Science, Hyderabad (BITS); Norwegian University of Science and Technology (NTNU); Norwegian Institute for Water Research (NIVA)

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The ACPS Group collaborates with highly reputed and top Indian Universities through the INTPART project INCAPS: Indo-Norwegian Collaboration in Autonomous Cyber-Physical Systems (INCAPS)

The INTPART (International Partnerships for Excellent Education and Research) Program, from the Research Council of Norway, funds partnerships between Norwegian higher education and research institutions and other excellent research partners in prioritized countries: Brazil, Russia, India, China, South Africa, USA, and Canada. The ACPS Group at the University of Agder was granted funding to collaborate with highly reputed Indian Universities in the field of Autonomous Cyber-physical Systems

Fig. 1. Indo-Norwegian collaboration, partners, activities and main areas of expertise.

https://www.forskningsradet.no/prognett-internasjonale-stipend/Nyheter/100_millioner_kroner_til_internasjonalt_forsknings_og_utdanningssamarbeid/1254038210541&lang=no.

The ACPS Research Group (https://acps.uia.no/) at the University of Agder collaborates with highly reputed and top Indian Universities and leads the INTPART project Indo-Norwegian collaboration in Autonomous Cyber-Physical Systems (INCAPS), with funding of around 6 MNOK from the Research Council of Norway. This project establishes a long-term collaboration between several top-ranked Indian Universities, including the Indian Institute of Science (IISc), Bangalore, Indian Institute of Technology, Hyderabad (IITH), International Institute of Information Technology, Hyderabad (IIITH), Birla Institute of Technology and Science (BITS), Hyderabad, BML Munjal University (Through Prof. M.B.Srinivas) and several Norwegian Universities and institutes, including University of Agder (UiA), Norwegian University of Science and Technology (NTNU) and Norwegian Institute for Water Research (NIVA) in world-class research and education.

Key active participants from the University of Agder are researchers from the ACPS Group, Prof. Linga Reddy Cenkeramaddi, Prof. Ajit Jha, and Prof. Jing Zhou. Other key researchers involved in the project are Prof. Kimmo Kansanen from NTNU, Dr. Christopher Harman from NIVA, Prof. Phaneendra K. Yalavarthy from the Department of computational data sciences, Indian Institute of Science, Bangalore, Prof. Abhinav Kumar from Indian Institute of Technology, Hyderabad, Prof. Soumya J. from Birla Institute of Technology and Science, Hyderabad, Prof. M B Srinivas from Birla Institute of Technology and Science, Hyderabad (Presently at BML University, Founded by Hero Group) and Prof. Lalitha Vadlamani from the International Institute of Information Technology, Hyderabad. Søren Kragholm at UiA will participate in the administrative coordination of the INCAPS project.

Major goals are to strengthen competitiveness and innovation capacity, solve major societal challenges, and develop high-quality academic environments. This project considers broad areas of research which include smart sensing for autonomous systems, mmWave sensors-based system design, analog, digital, and mixed signal circuit design, prototyping of wireless communication systems, low-altitude UAVs tracking and communications, de-centralized wireless communications, in-network processing and intelligence for heterogeneous wireless sensor and communication networks, machine learning and deep learning for autonomous systems, data analytics, energy harvesting based smart electronic systems, smart water networks and inference methods for timely detection and prediction of events, cognitive control and adaptive learning in autonomous cyber-physical systems.

The key objectives of the INCAPS project are the following: I). Strengthening collaborative network between industry (both public and private enterprises, small and medium-sized enterprises, and multi-national companies) and academia. II). Increased value creation and enhanced innovation by using smart sensing, machine, and deep learning techniques in autonomous cyber-physical systems. III). Facilitate education and knowledge sharing through better mobility for students and researchers. IV). Create an arena for the generation of research and innovation projects. V). Increased utilization of research and educational infrastructure both in Norway and India. VI). Integrate professionals from industries and academics through workshops, seminars, webinars, and summer/winter schools.

ACPS Group Website

INCAPS Project Website

INCAPS NFR Website

https://www.regjeringen.no/no/aktuelt/100-millioner-til-samarbeid-med-viktige-kunnskapsnasjoner/id2621219/

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ACPS Research Group along with top the Indian Institutes lead the Low-altitude UAV communication and tracking (LUCAT) project

  • 2018 – The Low-altitude UAV communication and tracking (LUCAT) project funded by INDNOR programme jointly by the Norwegian research council (NFR) and the Department of Science and Technology (DST), Project Manager and PI: Professor Linga Reddy Cenkeramaddi, ACPS Research Group, Department of ICT, UiA Grimstad, Project duration: 2019-2024.

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The LUCAT project aims to develop advanced and robust algorithms to detect and accurately trace rapidly moving UAVs, popularly called drones. The research takes place at the University of Agder’s ACPS Research Group, Department of Computational and Data Sciences (CDS), IISc Bangalore, India, the Robert Bosch Centre for Cyber-Physical Systems, IISc Bangalore, India, and the Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India. This is the only project where the prestigious Indian University IISc collaborates with a Norwegian university in relation to the areas of signal processing, communication technology, and machine learning.

Full project name: Low-altitude UAV Communication and Tracking (LUCAT)

Funding: Researcher Project, IKTPLUSS INDNOR Program, Research Council of Norway

Principal Managers: Prof. Linga Reddy Cenkeramaddi, Prof. Abhinav Kumar

and Prof. Phaneendra K. Yalavarthy.

Topic: The LUCAT project funded by IKTPLUSS-INDNOR (Joint Indo-Norwegian researcher projects within Information and Communication Technology) develops the technology for both communication and precise tracking of both manned and unmanned aerial vehicles operating in low-altitude corridors. The Autonomous and Cyber-Physical Systems (ACPS) research group at the University of Agder, Campus Grimstad, Norway in collaboration with the Department of Electrical Engineering at the Indian Institute of Technology, Hyderabad, India, and the Department of Computational and Data Science and Robert Bosch Centre for cyber-physical systems at the Indian Institute of Science, Bangalore, India, will jointly design, develop and implement the proposed technology. This project aims to detect and precisely track multiple rapidly moving unmanned aerial vehicles using smart radar sensors, as well as novel signal processing and wireless communication algorithms. New methods will be developed also to classify the objects in the flight corridors and the communication modules located within the unmanned aerial vehicles will be advanced software-defined radio modules with the ability to sense on-the-fly the radio-frequency environment, leading to the discovery of opportunities for communications (what is called spectrum cognizant communications). The tasks of tracking and communication will be cooperating and enhancing each other, improving substantially the performance of tracking and classification, as compared to the currently existing solutions.

Participant Institutions: ACPS-UiA, IISc Bangalore, IIT Hyderabad

Period: 2018 – 2024

Project details: In the near future, a large number of unmanned aerial vehicles, also known as drones, will pervade populated areas’ skies, serving millions of people worldwide for goods transportation, construction, agriculture, medical, surveillance, search-and-rescue operations, and a variety of other applications. Daily tasks such as food or packet delivery, grocery shopping, and surveillance, among others, will be carried out by autonomous UAVs in densely populated areas, resulting in profound changes in day-to-day human life. The LUCAT project, funded by IKTPLUSS-INDNOR (Joint Indo-Norwegian researcher projects in Information and Communication Technology by NFR and DST), is working to develop technology for accurate sensing, precise tracking, and communication of both manned and unmanned aerial vehicles operating in low-altitude corridors. We, the Autonomous and Cyber-Physical Systems Research Group at the University of Agder, Campus Grimstad, Norway, will design, develop, and implement the proposed technology in collaboration with the Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India, and the Department of Computational and Data Sciences, & the Robert Bosch Centre for Cyber-Physical Systems Indian Institute of Sciences, Bangalore, India. Using mmWave radar sensors and other sensors, as well as novel signal processing and machine learning models, and wireless communication algorithms, this project aims to sense/detect and precisely track multiple rapidly moving unmanned aerial vehicles. New methods will also be developed to classify objects in flight corridors, and communication modules located within unmanned aerial vehicles will include advanced software-defined radio modules with the ability to sense the radio-frequency environment on-the-fly, leading to the discovery of communications opportunities (what is called spectrum cognizant communications). Sensing, tracking, and communication tasks will collaborate and enhance each other, significantly improving sensing, tracking, and classification performance when compared to currently available solutions for low-altitude traffic management systems. New techniques for the detection, localization, and classification of UAVs are developed using ground station mmWave radars. Hybrid communication schemes are explored for UAV-UAV and UAV-Ground station communications.

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