According to the article titled "Updated science-wide author databases of standardized citation indicators" published in PLOS Biology by Dr. John PA Ioannidis, K. W. Boyack and J. Baas from Stanford University, 45 academicians from ITU ranked among the top 2% scientists in the world. Assoc. Dr. Özgür Atalay is also involved in the list. We congratulate him on his outstanding achievement.

Abstract: CardioVascular Diseases (CVDs) have a significant share over all medical problems. From this point of view, many studies have been conducted on heart diseases and different heartbeat monitoring systems have been developed. Although Electro-CardioGraphy (ECG) is the most widely used technique among other monitoring systems, ECG measurement with conventional electrodes have also many disadvantages that can be overcome if replaced with textile electrodes. This study involves creation of textile based ECG electrodes, related circuitry designs, signal processing, implementations of peak detection and heart rate calculation algorithms and finally, a real time ECG monitoring application. Moreover, Beat Per Minute (BPM) calculation and comparison of these values with existing ECG devices have been investigated.

Abstract: One of the main challenges of navigation systems is the inability of orientation and insufficient localization accuracy in indoor spaces. There are situations where navigation is required to function indoors with high accuracy. One such example is the task of safely guiding visually impaired people from one place to another indoors. In this study, to increase localization performance indoors, a novel method was proposed that estimates the step length of the visually impaired person using machine learning models. Thereby, once the initial position of the person is known, it is possible to predict their new position by measuring the length of their steps. The step length estimation system was trained using the data from three separate devices; capacitive bend sensors, a smart phone, and WeWALK, a smartcane developed to assist visually impaired people. Out of the various machine learning models used, the best result obtained using the K Nearest Neighbor model, with a score of 0.945 R^2 . These results support that indoor navigation will be possible through step length estimation.