News & Events

 

Traditionally, the authors of the best oral and poster presentations were awarded after the conference. We are glad that this year two FTMC young scientists are among them!

 

Aivaras Špokas, an engineer from the FTMC Department of Optoelectronics, has been awarded for the best oral presentation; Erikas Cicėnas, a technician from the FTMC Department of Fundamental Research, also won in this category.

 

Congratulations to our colleagues, and good luck in continuing the important work you have started!

 

 

 

A. Špokas presented the topic "GaAsBi Based NIR Emitters for Blood Analyte Monitoring".

 

This work is part of a Lithuanian-Latvian-Taiwanese project, which aims to improve pulse oximetry devices. A pulse oximeter is a non-invasive medical device that measures the oxygen level in the blood. It is usually used by attaching it to a finger or other part of the body. The oximeter sends out weak pulses of light that pass through the person's tissues and the sensor detects how much oxygen is in the blood.

 

 

 

"Although the technology is quite mature, there are still accuracy issues when it comes to integrated oximeters (such as those found in smartwatches) that operate in reflection mode. Our proposed solution is a dual-wavelength (800 and 1100 nanometre) laser system operating in the near infrared (NIR).

 

It was at the Open Readings conference that emitters were presented for the longer wavelengths at 1070 nanometres (light emitting diode, LED) and 1142 nanometres (laser diode, LD). The emitters were grown in the lab on a separating layer and integrated on a silicon platform. The material chosen for the active area is gallium arsenide bismide (GaAsBi), known for its temperature stability and other useful properties. It is noteworthy that our room-temperature laser diode light beam of 1142 nanometres matches the world's record wavelength for a GaAsBi quantum-well laser, which has been described and published in the literature," says Aivaras.

 

 

 

Meanwhile, E. Cicėnas spoke on "Deep Learning for Star Cluster Detection in Galaxies".

 

One of the most important objects when studying galaxies are stellar clusters. The stars in a cluster are of a similar age, composition, and distance, thereby making them exceptionally suitable to learn about a galaxy’s star formation and dynamic history.

 

However, detecting star clusters in the Universe is not easy: there are still no reliable methods to allow computers to detect them autonomously, and methods using experts or volunteers are employed. For this reason, Erikas with his coleagues seek to employ modern machine learning algorithms, specifically a convolutional neural network (a model that tries to simulate the way the brain works), to automate the detection of clusters.

 

The hope is that this artificial intelligence-based method will allow the desired star clusters to be quickly and repeatedly detected in the images.  

 

 

 

"To train the model we use a large dataset of synthetic clusters [computer-generated models that simulate the evolution of real clusters] covering a variety of ages, masses, and shapes. We project these formations onto real observations from the Panchromatic Hubble Andromeda Treasury, a project to study the Andromeda Galaxy.

 

We evaluate the performance of our model using various tests involving both synthetic and natural clusters. We compare these results to existing catalogues. We will also draw attention to the difference in performance by volunteers and our model, " says the young FTMC astrophysicist.

 

In his presentation, he explained how the deep learning model he and his colleagues have developed works, how the experiments have gone, and what the biggest challenges and opportunities for improvement remain.