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Association between electroencephalography (EEG) and individually personal information is being explored by the scientific community. Though person identification using EEG is an attraction among researchers, the complexity of sensing limits using such technologies in real-world applications. In this research, the challenge has been addressed by reducing the complexity of the brain signal acquisition and analysis processes. This was achieved by reducing the number of electrodes, simplifying the critical task without compromising accuracy. Event-related potentials (ERP), a.k.a. time-locked stimulation, was used to collect data from each subject's head. Following a relaxation period, each subject was visually presented a random four-digit number and then asked to think of it for 10 seconds. Fifteen trials were conducted with each subject with relaxation and visual stimulation phases preceding each mental recall segment. We introduce a novel derived feature, dubbed Inter-Hemispheric Amplitude Ratio (IHAR), which expresses the ratio of amplitudes of laterally corresponding electrode pairs. The feature was extracted after expanding the training set using signal augmentation techniques and tested with several machine learning (ML) algorithms, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN). Most of the ML algorithms showed 100% accuracy with 14 electrodes, and according to our results, perfect accuracy can also be achieved using fewer electrodes. However, AF3, AF4, F7, and F8 electrode combination with kNN classifier which yielded 99.0±0.8% testing accuracy is the best for person identification to maintain both user-friendliness and performance. Surprisingly, the relaxation phase manifested the highest accuracy of the three phases.

We are currently facing a “labor crisis,” particularly in the field of logistics, because of reductions in the labor force. Therefore, industries must make their logistics more efficient by utilizing autonomous mobile robotics technologies. This paper proposes a hierarchized map concept that makes unmanned delivery tasks which use multiple autonomous robots more efficiently. Using our proposed concept, an autonomous mobile robot can move automatically on a more efficient path than using current methods. In addition, the management platform for autonomous robots can be used to prevent accidents such as collisions or deadlocks between autonomous robots.

Identifying the coping mechanisms practiced by the medical students is of importance and must be explored to promote healthy stress coping methods. Hence, a descriptive cross-sectional study was undertaken with a sample of 410 medical students from a State Medical School in Sri Lanka. The research question of the study focused on identifying the different coping methods practiced by medical students. A questionnaire with 2 parts, A and B, was used to collect data on stress coping methods used by the study participants. The study also obtained data on the effectiveness of popular stress coping methods from the users. The responses for the questions in part A showed a trend of positive outlook in all general aspects of life except for one question, which directly questioned the ability of students to cope with stress imposed by the academic programme. Based on the results from part B of the questionnaire, the most common and widely used stress coping mechanisms included sleeping, listening to music and talking with friends and family. Effective coping strategies identified to minimize stress were religious activities like praying/worshiping and meditation. Moreover, seeking advice from lecturers, seniors, going home and engaging in sports and games were also found to be effective in lessening stress. In conclusion, facilities for students to involve in religious activities such as prayers and meditation shall be available in the Faculty. Getting counseling and involving in sports also appear to alleviate stress and these should be integrated into the life of medical students.