Nima L. Wickramasinghe from the Department of Electronic and Telecommunication Engineering (ENTC), together with his mentor Mohamed Athif from the Department of Biomedical Engineering, Boston University (previously an undergraduate at ENTC) has won the 3rd place in 3 categories (All-lead, 3-lead, 2-lead) in the Physionet/Computing in Cardiology Challenge 2021.
PhysioNet supports challenges, which invite participants from all over the world from various institutes to tackle clinically interesting questions that are either unsolved or not well-solved. In cooperation with the Computing in Cardiology conference, PhysioNet has been co-hosting a challenge annually. This year, the conference was held in Brno, Czech Republic from 12th to 15th of September in a hybrid manner. Computing in Cardiology (formerly Computers in Cardiology) is an international scientific conference that has been held annually since 1974. CinC provides a forum for scientists and professionals from the fields of medicine, physics, engineering, and computer science to discuss their current research in topics pertaining to computing in clinical cardiology and cardiovascular physiology.
This year’s challenge was to identify Cardiac abnormalities (26 scored) given the ECG data of the patients. Usually, the ECG data consists of the signals from the 12-leads. But, this year’s challenge focused on whether the same accuracy can be achieved using reduced-lead ECG data. Using a smaller number of leads would enable low-cost, portable, and user-friendly point of care devices.
The team (team NIMA) proposed a novel solution to tackle this Multi-label classification problem by creating a Deep convolutional neural network that used the time domain and frequency domain of the ECG signals to classify the 26 scored cardiac abnormalities. The results showed that reduced-lead ECG data can obtain almost the same accuracy obtained using all leads.
The team was able to obtain 3rd place in the All-lead, 3-lead, and 2-lead categories competing against 39 International teams. The 2nd place was obtained by team DSAIL_SNU, mainly from Seoul National University. And, the 1st place was obtained by team ISIBrno-AIMT, mainly from the Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.
Remarks: The collaboration between Nima L. Wickramasinghe and Athif Mohamed was facilitated by the ScholarX program from the Sustainable Education Foundation. ScholarX is a 6-month program for Sri-Lankan undergraduates who would like to get free premium mentoring during their study period.
Link to challenge: https://physionetchallenges.org/2021/Read More
A team of undergraduate students (Team NFPUndercover) from the Department of Electronic and Telecommunication Engineering (ENTC) under the supervision of Dr. Chamira Edussooriya from ENTC, University of Moratuwa, became the 2nd runner up in the IEEE Video and Image Processing Cup (VIP Cup) 2021 competition at the 28th International Conference on Image Processing (ICIP) 2021. ICIP is an annual flagship conference of the IEEE Signal Processing Society which is one of the world’s premier associations for signal processing engineers, academics, and industry professionals. This year the conference was held from the 19th to 22nd of September 2021, virtually in Anchorage, Alaska, USA. The IEEE VIP Cup competition is one of the most prestigious competitions in video and image processing for undergraduate students. This year’s task was to develop a robust algorithm to estimate in-bed human poses under heavy occlusion caused by blankets and varying illumination conditions. This algorithm can be utilized for in-bed human behavior monitoring during sleep or rest state, which is crucial for prognostic, diagnostic, and treatment of many healthcare complications. Team NFPUndercover proposed a novel solution for this task leveraging multiple approaches from both computer vision and signal processing such as extreme occlusion-based data augmentation and label smoothening via knowledge distillation. Specifically, their approach is feasible to implement in a resource-constrained setup. Image Processing and Machine Vision is a branch of Artificial Intelligence that models and learns the image representations to autonomously accomplish downstream tasks such as Image Classification, Object Detection, Semantic Segmentation, etc. It utilizes advanced statistical and mathematical algorithms to make a machine see and understand the real world as humans do. This achievement by UoM places Sri Lanka at a higher position in the world of the image processing communityRead More
Team DigitX, a team of ten undergraduates from the Department of Electronic and Telecommunication Engineering (ENTC) under the supervision of Dr. Chamira Edussooriya from ENTC, University of Moratuwa, emerged as the champions of the Students Challenge at the Internal Conference on Autonomous Systems (ICAS) 2021 conference. The conference was held virtually in Montreal, Canada from August 11 to 13, 2021.
ICAS 2021 is the premier international forum organized by the IEEE Signal Processing Society (SPS) through IEEE SPS Autonomous Systems Initiative, for presenting the technological advances and research results in the fields of theoretical, experimental and applied autonomous systems. IEEE ICAS was held for the first time this year and it focused on providing an updated state-of-the-art over advanced signal processing theories and techniques that are relevant for developing autonomous systems.
The ICAS 2021 Students Challenge was on “unsupervised anomaly detection through self-aware autonomous systems”, to detect abnormal events during the navigation of a ground/ aerial vehicle. The team was able to develop an unsupervised learning algorithm with state-of the-art signal processing and machine learning techniques such as auto-encoders, recurrent neural networks for time-series data signal processing.
Based on the inertial measurement unit (IMU) sensor data, video data captured by the cameras of autonomous systems in several camera perspectives and light detection and ranging (LIDAR) sensor data (point clouds), the unsupervised model was trained to identify the normal situations that it saw during the training phase as normal and every other situation as abnormal (as per the challenge requirement a reference normal scenario was defined for each dataset). UoM won the first place while a team from Indian Institute of Technology (IIT), India and a team from CentralSupelec, France won first and second runners up, respectively.
An autonomous system is an artificial system capable of performing a set of tasks with a high degree of autonomy. Developing computing systems with advanced levels of autonomy has been a crucial task for decades in order to manage ever-increasing requirements in complexity. This achievement by UoM places Sri Lanka at a higher position in the world signal processing community.Read More
A team of undergraduate students (Team T- Cubed) from the Department of Electronic and Telecommunication Engineering (ENTC) under the supervision of Dr. Prathapasinghe Dharmawansa from ENTC, University of Moratuwa, won the first place in the IEEE Signal Processing Cup 2021 competition at the ICASSP 2021 conference.Read More
Sri Lankan Robotics Challenge (SLRC) is one of the most glorious and iconic events organized by the Electronic Club of Department of Electronic and Telecommunication Engineering, University of Moratuwa. The 8th edition of SLRC commenced on 19th of October 2019 with the unwavering guidance of the head of the department Dr. Nuwan Dayananda, patron of Electronic Club Eng. Kithsiri Samarasinghe, senior treasurer and staff advisor of Electronic Club Dr. Ranga Rodrigo, senior lecturer Dr. Peshala Jayasekara and the president of Electronic Club Mr. Matheesha Abeysekera. This year’s event was chaired by the assistant secretary of Electronic Club Mr. Kavish Ranwella and the committee member Mr. Thushara Sampath. The event was planned to create the greatest platform for the competitors to showcase their talents.
SLRC 2019 inaugurated its golden days with the university category preliminary round. 16 teams representing universities from all over the country actively participated in this round, from which 6 teams were selected for the final round after the careful and excellent judgement made by the judges of the day, senior lecturer at KDU Eng. Udaya Dampage, Electronic engineer at Zone 24×7 Mr. Kanishka Wijayasekara and lecturer of University of Moratuwa Mr. Thilina Ambagahawaththa. A new award was introduced in this year’s challenge with the sole purpose of encouraging the competitors even more. Namely, ‘The Best Robot Design’ award was secured by the team Circuit Breakers from University of Moratuwa.
The next day, 20th of October, 2019 was dedicated for the final round of the university category where 6 marvelous teams performed head to head with each other for the championship. With the honorable presence of the judges of the day, director and head of Electronics High Tech Lanka International Mr. Salinda Tennakoon, senior lecturer of University of Moratuwa Dr. Peshala Jayasekara and senior lecturer of University of Moratuwa Dr. Tharaka Samarasinghe winning teams were selected. Team Neo-RX from ICBT Campus, Kandy secured the winning title achieving the best overall scores and the most outstanding performance of the day. Winner was awarded with a cash prize of Rs.100,000. Team Circuit Breakers from University of Moratuwa won second place securing a cash prize of Rs.60,000. Team White Walkers representing University of Ruhuna became 2nd runners up winning a cash prize of Rs.30,000. University category was a huge success exhibiting all the hard work put in by the Department of Electronic and Telecommunication Engineering to build a broad platform for the talented youth of the country. SLRC provides these youthful and fresh minds a golden opportunity to interact with peers who bear the same interests as well as industry professionals and other resource persons.
21st of October 2019 was the last day of the challenge. School category first round started off with a huge rate of participation from 52 teams representing schools around the country. Team Aloyz from St. Aloysius College emerged as the winners of the title ‘The Best Robot Design’. Final round was held in the evening with the judges of the day, lecturer of University of Moratuwa Mr. Kavisha Vidanapathirana, lecturer of University of Moratuwa Mr. Asanka Rathnayaka and lecturer of University of Moratuwa Mr.Heshan Fernando. Their substantial commitment made SLRC 2019 a triumph. Team Infinity from Maliyadeva College won the first place with a cash prize of Rs.50,000. Team Galaxy from Wycherley International School and team Neo-revolt from Maliyadeva Model School secured second and third places with cash prizes of Rs.30,000 and Rs.15,000 respectively.
SLRC 2019 concluded on a high note creating a room for wisdom to shine. It gathered wisdom from all over the country, shared knowledge and vision, encouraged the purposeful hearts and finally ended continuing the legacy of the Department of Electronic and Telecommunication Engineering, University of Moratuwa.Read More
The IEEE Communications Society holds an annual student competition, encouraging communications engineering students to expand their knowledge, test and showcase new skills, and inspire innovation. IEEE ComSoc Student Competition 2019 was held with theme ‘Communications Technology Changing the World Student Competition’. Two teams; AKMA Bionics and RcubeH from the Department of Electronic and Telecommunication Engineering were able to secure Honorary Mentions (being ranked among the top 15 in the world) at the IEEE ComSoc Student Competition 2019.
Team ‘AKMA Bionics’ comprised of 4 final year undergraduates, Ashwin De Silva, Malsha Perera, Kithmin Wickramasinghe and Asma Naim. Their project was titled ‘Wearable Cost Effective Wireless Dry Contact sEMG Sensor System for Controlling Digital Technologies’. Surface electromyograms (sEMG) is a biopotential recording technique by which we record a muscles’ electrical activity from the surface of the skin; which reflects the generation and propagation of Motor Unit Action Potentials. This project focuses on designing a dry contact electrode for acquiring sEMG signals from forearm, including the design and development of the interfacing systems and circuits for the muscle-computer interface (MCI), under a reasonable cost with the intention of creating a novel finger gesture based wireless interaction device using modern communication technologies. The sEMG signals acquired from the dry contact electrode system is used for real-time hand motion recognition. These recognised hand motions will be used to control different digital technologies such as a smart switch, a smart light bulb, a computer, a smartphone and most importantly a bionic arm. Current available devices for acquisition of sEMG have shortcomings in terms of design, speed, reusability and reconfigurability. The dry contact, stainless steel based electrode system presented by this team is designed in order overcome the shortcomings of the currently available sEMG acquisition systems.
Team ‘RcubeH’ comprised of 4 final year undergraduates, Ranjula Hettiarachchi , Rashinda Wijethunga, Ravindu Rashmin, Hashini De Silva. Their project was titled ‘Indoor Navigation for Visually Impaired’. At present there are over 30 million people around the world who are completely blind. Navigation at an unknown location has been one of their biggest challenges. Even though many outdoor navigation technologies have been invented to aid them in outdoor navigation, no widely adapted method has been launched to support the indoor navigation. Team RcubeH presents the idea of developing a method which uses only the proximity to a Bluetooth beacon and combines that information with mobile phone sensor data to navigate the visually impaired person along a certain route, so a major advantage of this method is number of beacons needed for a building can be reduced from a significant number. The team has developed filtering mechanisms and machine learning algorithms to obtain accurate distance estimation with high accuracy and robustness. Another advantage of the proposed method is the customizability, when the system is designed it will be adaptable to various buildings and various common features of a certain building without needing extra training/modification of algorithms. The beacons are placed strategically around limited points of interest around the building and use the mobile phones’ built-in sensors to navigate through these points of interest. The beacons are only used for marking a certain landmark and only the proximity (distance to a certain beacon) is used to for the navigation process. The main advantage of this approach is using a limited number of BLE beacons which reduces the cost by a large portion. Even though similar BLE proximity-based approaches have been taken by some researchers, the measures they have taken to increase the system’s accuracy and customizability is rather low. The team is intending to optimize the distance estimation process by implementing machine learning and filtering techniques and make the system customizable to different building environments. Once the system is developed it can be implemented in any building with a simple architectural design and a visually impaired person will be able to navigate to a certain destination inside the building. This system can be further improved to be used by the general public.Read More