Engineering can enhance students' theoretical knowledge and practical application skills when integrated into K‐12 education. This approach encourages students to contemplate technology and engineering in more depth. Field programmable gate array (FPGA) technology training is becoming increasingly important, especially for vocational high school technical teachers. This study focuses on providing FPGA technology training to these vocational and technical teachers. The aim is to improve their theoretical understanding of FPGA digital design and practical application skills. By incorporating the latest technology into their courses, educators can offer students in‐depth learning and practical experience in electronic engineering and digital design. The training program covered the basics of FPGA, its applications, and how it can benefit vocational high schools in laboratory environments. A total of 34 technical teachers participated in the training program. The effectiveness of the program was evaluated through pre‐ and posttraining questionnaires. The results showed that most of the questionnaire items were statistically and practically significant and varied according to the teachers' department, teaching experience, and level of education.

Brain-computer interfaces (BCIs) offer promising solutions for assisting individuals with disabilities, supporting neurorehabilitation, and enhancing human capabilities. However, the limited decoding accuracy of EEG-based motor imagery (MI) signals poses a major challenge for the practical deployment of BCI systems. A common approach involves using signals from opposite hemispheres to boost classification accuracy. Yet, to control devices such as robotic hands or prosthetics in a human-like manner, it is essential to accurately classify hand opening and closing tasks using EEG signals from the same motor cortex region. This study introduces TransformerNet, a novel deep learning architecture specifically designed to classify hand open-close MI tasks from the same brain region. The task is particularly challenging due to the high similarity and overlapping nature of the EEG signals. TransformerNet combines a convolutional module, inspired by EEGNet, to extract local spatial features, with a Transformer encoder that captures long-range temporal dependencies. Furthermore, a channel attention mechanism enhances the model’s ability to focus on the most informative features. In experimental evaluations, TransformerNet achieved an average classification accuracy of 85.97%, outperforming traditional deep learning methods. The model effectively captures high-level temporal-spectral patterns and uncovers hidden dependencies within the EEG signals. These results demonstrate the potential of integrating attention mechanisms with Transformer-based architectures to improve MI-based BCI performance. This advancement holds promise for real-world applications such as brain-controlled prosthetics, assistive devices, and human-computer interaction, moving BCI technologies closer to practical and reliable implementation.

This study aims to investigate the effects of the Robotic Hand Exoskeleton system on spasticity, motor control skills, activities of daily living, quality of life, and functional independence in stroke patients. A randomized controlled trial was conducted with 36 stroke patients aged 35-85, divided into two groups: conventional physiotherapy (n = 18) and robotic rehabilitation (n = 18). Evaluations included the Modified Ashworth Scale (MAS) for spasticity, Motor Activity Log 28 (MAL28) for daily activities, Frenchay Arm Test for motor control, and Stroke Impact Scale 3.0 (SIS 3.0) for quality of life and functional independence. Both groups received 15 treatment sessions over five weeks, with the robotic rehabilitation group additionally using a Robotic Hand Exoskeleton for 10 minutes per session. A significant improvement was observed in MAS (ES=0.092), MAL28, Frenchay Arm Test (ES=0.093), and SIS 3.0 (ES=0.01) scores after treatment (p < 0.05). However, no statistically significant differences were found between the groups (p > 0.05). Robotic hand rehabilitation improved motor function, daily activities, quality of life, and reduced spasticity, but showed no superiority over conventional physiotherapy. Both treatments demonstrated similar effectiveness, with robotic rehabilitation offering potential financial and time-saving benefits for healthcare providers.

The acceptance of advanced prosthetic systems by users requires overcoming unique challenges of fitting prostheses to unique user anatomies to achieve systematic performance across a user base. Variations among individuals introduce complexities in fitting the sockets. Due to the difficulty of measuring socket interface characteristics, there is a lack of quantifiable diagnostic fitment information available. As a result, the process of fitting sockets is currently a laborintensive, manual approach, and can often result in sockets that are uncomfortable, unstable, or impede full range of motion. Additionally, results can be difficult to reproduce reliably. A diagnostic tool has been developed to quantify the relative movement between the socket and the residual bone during the fitting process. The approach leverages low cost and high precision ultrasound transceivers and intuitive visualization software to provide quantifiable socket fitment data. The goal is to enable a systematic socket-fitting strategy that yields reliable and reproducible results. Human subject testing and results are presented that show movement tracking relative to a cuff with an ultrasound transducer with an RMSD of 0.36 mm.

Aim: In real-time control of prosthesis, orthosis, and human–computer interface applications, the displacement of surface electrodes may cause a total disruption or a decline in the classification rates. In this study, a constrained independent component analysis (cICA) was used as an alternative method for addressing the displacement problem of surface electrodes. Materials and Methods: The study was tested by classifying six-hand gestures offline and in real-time to control a robotic arm. The robotic arm has five degrees of freedom, and it was controlled using surface electromyography (sEMG) signals. The classification of sEMG signals is realized using artificial neural networks. cICA algorithm was utilized to improve the performance of classifiers due to the negative effect of electrode displacement issues. Results: In the study, the classification results of the cICA applied and unapplied sEMG signals were compared. The results showed that the proposed method has provided an increase between 4% and 13% in classifications. The average classification rates for six different hand gestures were calculated as 96.66%. Conclusions: The study showed that the cICA method enhances classification rates while minimizing the impact of electrode displacement. The other advantage of the cICA algorithm is dimension reduction, which is important in real time applications. To observe the performance of the cICA in the real-time application, a robotic arm was controlled using sEMG signals.

Elektroensefalografi beyindeki elektriksel akımın ölçülmesi ile elde edilen sinyallerdir. Bu sinyallerin sınıflandırılması özellikle beyin sinyalleri ile ilgili rahatsızlıkların teşhis, tanı ve tedavisine katkı sağladığı için önemlidir. Ancak bu sinyallerden anlamlı sonuçlar elde edebilmek için öncelikle veri temizleme, öznitelik çıkarma ve öznitelik seçme yöntemleri kullanılmıştır. Daha sonra bu yöntemler sınıflandırma başarısına katkıları açısından kıyaslanmıştır. İlk olarak filtrelenen veriden Ayrık Dalgacık Dönüşümü metodu ile istatistiksel özellikler çıkarılmış, ardından Diferansiyel Evrim Algoritması kullanılarak en iyi sınıflandırma sonucunu veren öznitelik alt kümesi seçilmiştir. Seçilen özniteliklere sahip veri kümesinin sınıflandırma başarısı Destek Vektör Makineleri ile test edilmiştir. Kullanılan yöntem ile bazı sınıfların ayrılmasında benzer çalışmalardan daha iyi sonuçlar elde edilmiştir.