Dr. Ambarish Kulkarni
Swinburne University of Technology, Melbourne, Victoria, Australia
'%3e%3cpath%20d='M22.1642%2019.0924C21.7317%2018.068%2021.1041%2017.1375%2020.3163%2016.3527C19.5309%2015.5656%2018.6006%2014.9381%2017.5766%2014.5048C17.5674%2014.5002%2017.5582%2014.4979%2017.5491%2014.4933C18.9774%2013.4616%2019.9059%2011.7811%2019.9059%209.88504C19.9059%206.74408%2017.3611%204.19922%2014.2201%204.19922C11.0792%204.19922%208.53429%206.74408%208.53429%209.88504C8.53429%2011.7811%209.46282%2013.4616%2010.8912%2014.4956C10.882%2014.5002%2010.8728%2014.5025%2010.8636%2014.5071C9.83652%2014.9404%208.91487%2015.5617%208.1239%2016.355C7.33685%2017.1404%206.70933%2018.0707%206.276%2019.0947C5.85031%2020.0972%205.62072%2021.172%205.59967%2022.2609C5.59905%2022.2853%205.60335%2022.3097%205.61229%2022.3325C5.62123%2022.3553%205.63465%2022.376%205.65174%2022.3935C5.66883%2022.4111%205.68926%2022.425%205.71182%2022.4345C5.73437%2022.444%205.7586%2022.4489%205.78308%2022.4489H7.15868C7.25956%2022.4489%207.3398%2022.3686%207.3421%2022.2701C7.38795%2020.5001%208.09868%2018.8425%209.35506%2017.5861C10.655%2016.2862%2012.3814%2015.5709%2014.2201%2015.5709C16.0588%2015.5709%2017.7852%2016.2862%2019.0852%2017.5861C20.3415%2018.8425%2021.0523%2020.5001%2021.0981%2022.2701C21.1004%2022.3709%2021.1807%2022.4489%2021.2815%2022.4489H22.6571C22.6816%2022.4489%2022.7058%2022.444%2022.7284%2022.4345C22.751%2022.425%2022.7714%2022.4111%2022.7885%2022.3935C22.8056%2022.376%2022.819%2022.3553%2022.8279%2022.3325C22.8369%2022.3097%2022.8412%2022.2853%2022.8406%2022.2609C22.8176%2021.165%2022.5907%2020.0989%2022.1642%2019.0924ZM14.2201%2013.8284C13.1678%2013.8284%2012.1773%2013.418%2011.4322%2012.6729C10.6871%2011.9278%2010.2767%2010.9374%2010.2767%209.88504C10.2767%208.83271%2010.6871%207.84227%2011.4322%207.09715C12.1773%206.35204%2013.1678%205.94165%2014.2201%205.94165C15.2724%205.94165%2016.2629%206.35204%2017.008%207.09715C17.7531%207.84227%2018.1635%208.83271%2018.1635%209.88504C18.1635%2010.9374%2017.7531%2011.9278%2017.008%2012.6729C16.2629%2013.418%2015.2724%2013.8284%2014.2201%2013.8284Z'%20fill='%238590A2'/%3e%3c/g%3e%3c/svg%3e)
Co-authors
Following
Followers
Research Focus & Metrics
Explore full metrics on Scilit
Academic Career Length
16Years (2011-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 August 2019 in Future Generation Computer Systems
Journal Article
MAMahbuba Afrin
Mahbuba Afrin
JJJiong Jin
Jiong JinARAshfaqur Rahman
Ashfaqur RahmanYTYu-Chu Tian
Yu-Chu TianAmbarish KulkarniJournal Article
Shuva PaulMRMuhtasim Riffat
Muhtasim Riffat
Abrar YasirMMMir Nusrat Mahim
Mir Nusrat MahimBSBushra Yasmin Sharnali
Bushra Yasmin SharnaliAmbarish Kulkarni +2
At present, the whole world is transitioning to the fourth industrial revolution, or Industry 4.0, representing the transition to digital, fully automated environments, and cyber-physical systems. Industry 4.0 comprises many different technologies and innovations, which are being implemented in many different sectors. In this review, we focus on the healthcare or medical domain, where healthcare is being revolutionized. The whole ecosystem is moving towards Healthcare 4.0, through the application of Industry 4.0 methodologies. Many technical and innovative approaches have had an impact on moving the sector towards the 4.0 paradigm. We focus on such technologies, including Internet of Things, Big Data Analytics, blockchain, Cloud Computing, and Artificial Intelligence, implemented in Healthcare 4.0. In this review, we analyze and identify how their applications function, the currently available state-of-the-art technologies, solutions to current challenges, and innovative start-ups that have impacted healthcare, with regards to the Industry 4.0 paradigm.
8 October 2018 in IEEE Transactions on Industrial Informatics
Journal Article
Akhlaqur RahmanJJJiong Jin
Jiong JinACAntonio L. Cricenti
Antonio L. Cricenti
ARAshfaqur Rahman
Ashfaqur RahmanAmbarish KulkarniTCTony Cricenti
Tony Cricenti
The fourth industrial revolution introduces an ideal opportunity for inclusion of cloud-enabled robots in factory environment to improve productivity and reduce human intervention. For this novel paradigm, task offloading plays a critical role in leveraging computation support from resourceful cloud infrastructure. In particular, network connectivity and on-demand mobility of robot significantly influence the task offloading decision-making and vice-versa. While current studies in literature separately consider mobility or communication aspects to accommodate offloading, ours is the first approach to integrate these three interdependent factors together in order to formulate a joint optimization problem for the proposed oil factory maintenance application. A modified genetic algorithm scheme is then developed to solve the problem with novel 3-layer decision: task offloading, path planning and access point selection. Simulation results and comparison with existing techniques suggest that communication-aware and mobility-driven offloading in industrial scenario leads to superior system performance and minimum consumption of resources.
5 October 2017 in Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Journal Article
Electric vehicles (EVs) are an alternative architecture in the automotive industry that provide reduced emissions. This research has developed a switch reluctance motor (SRM) in-wheel drivetrain for an EV. SRM drivetrains are cheaper and do not use rare earth elements unlike a permanent magnet motor (PMM). Conversely, the in-wheel SRM has a drawback of an increased mass on the suspension when compared with an equivalent power output PMM drivetrain. This situation results in an increased mass at the wheels; hence, a suspension analysis is required. This paper discusses the suspension dynamics evaluated using a quarter-car simulation of an in-wheel SRM EV and compares it to the internal combustion engine (ICE) vehicle. The simulation used step loads derived design scenarios, namely (1) sprung, (2) unsprung and (3) driver’s seat. Further Bode plot analysis techniques were used to determine the ride comfort range for the developed EV.
Journal Article
CSCristobal Sierra
Cristobal SierraShuva PaulAkhlaqur RahmanAmbarish KulkarniA road network is the key foundation of any nation’s critical infrastructure. Pavements represent one of the longest-living structures, having a post-construction life of 20–40 years. Currently, most attempts at maintaining and repairing these structures are performed in a reactive and traditional fashion. Recent advances in technology and research have proposed the implementation of costly measures and time-intensive techniques. This research presents a novel automated approach to develop a cognitive twin of a pavement structure by implementing advanced modelling and machine learning techniques from unmanned aerial vehicle (e.g., drone) acquired data. The research established how the twin is initially developed and subsequently capable of detecting current damage on the pavement structure. The proposed method is also compared to the traditional approach of evaluating pavement condition as well as the more advanced method of employing a specialized diagnosis vehicle. This study demonstrated an efficiency enhancement of maintaining pavement infrastructure.
1
10 August 2021 in IEEE Transactions on Network Science and Engineering
Journal Article
MAMahbuba Afrin
Mahbuba AfrinJJJiong Jin
Jiong JinARAshfaqur Rahman
Ashfaqur RahmanAGAndrea Gasparri
Andrea GasparriYTYu-Chu Tian
Yu-Chu TianAmbarish KulkarniCloud-aided robots are increasingly adopted in realizing Cyber-Physical-Social System (CPSS) to reduce human efforts and enhance the system performance with diverse robotic applications. However, the multi-hop distance from robot to cloud data centre elevates the data transfer delay that often inhibits the deadline-satisfied task execution of robotic applications. Thanks to edge computing, this issue can be well addressed by harnessing computing resources in proximity of data sources. As edge resources are constrained in energy and processing capacity, sharing resources among the tasks of multiple robotic applications is critical to ensure. Therefore, in this paper, we develop a congestion game-theoretic robotic edge resource allocation mechanism for CPSS, which not only maintains the Quality-of-Service (QoS) by meeting task completion deadlines but also satisfies the energy constraints of resources. Here, Agriculture 4.0 is considered as a use case for the proposed mechanism which can be extended for other domains. Nevertheless, the performance evaluation of the proposed mechanism is conducted in an iFogSim simulated edge computing environment. In comparison with existing greedy, heuristic, and evolutionary benchmarks, our mechanism is proven to offer overall 20\% improvement in deadline satisfaction, energy consumption, and resource utilization.
About Me
Dr. Ambarish Kulkarni is a Senior Lecturer and Director Vehicle Engineering Team at Swinburne University of Technology (SUT), specializing in engineering informatics with a focus on mobility, cognitive twins, and critical infrastructure applications. He is a fellow member of Engineers Australia and IEEE, and FSAE-Asia pacific. He also holds adjunct and honorary professor positions at IIT Hyderabad and Sanjay Ghodawat University India. He has worked with Swinburne for the last 13 years as an academic, contributing toward the education and research within the department of Mechanical and Product Design Engineering. Over the years, he has been involved in cutting-edge research with multiple high-profile industries (e.g., Downer EDI), providing his expertise in implementing digital transformations. His industry experience in product design, combined with his knowledge of design tools, encouraged the university to appoint him as the coordinator for product design engineering subjects from 2014 to 2017. In 2017, he was promoted to Senior Lecturer in computer-aided engineering (CAE) and lead academic advisor for SUT’s iconic formula society of automotive engineers (FSAE) race car team. Currently, he has been working on engineering informatics to enhance processes for human-centered products using specialized digital technologies (virtual and augmented reality).