The conversion of brain activity into text using electroencephalography (EEG) has gained significant traction in recent years. Many researchers are working to develop new models to decode EEG signals into text form. Although this area has shown promising developments, it still faces numerous challenges that necessitate further improvement. It is important to outline this area’s recent developments and future research directions to provide a comprehensive understanding of the current state of technology, guide future research efforts, and enhance the effectiveness and accessibility of EEG-to-text systems. In this review article, we thoroughly summarize the progress in EEG-to-text conversion. Firstly, we talk about how EEG-to-text technology has grown and what problems the field still faces. Secondly, we discuss existing techniques used in this field. This includes methods for collecting EEG data, the steps to process these signals, and the development of systems capable of translating these signals into coherent text. We conclude with potential future research directions, emphasizing the need for enhanced accuracy, reduced system constraints, and the exploration of novel applications across varied sectors. By addressing these aspects, this review aims to contribute to developing more accessible and effective Brain-Computer Interface (BCI) technology for a broader user base.

In recent years, the rapid growth of IoT devices has posed challenges for traditional cloud computing architectures due to issues related to latency, bandwidth, and real-time response. To overcome those constraints, fog computing architecture emerged as a promising solution, responding to IoT device requests at the edge and forwarding them to the cloud only when necessary. However, some requests still need to be sent to the cloud, making them susceptible to the shortcomings of cloud-based systems. To enhance the fog computing approach, this research proposes an integrated peer-to-peer (P2P) architecture within the fog layer. The proposed model builds upon the 2-layer LDEPTH architecture for P2P networks, wherein current networks are organized hierarchically, with each of the networks having a diameter of one overlay hop. These modest diameters play a crucial role in the new P2P fog model, as they facilitate highly effective data lookup techniques. In this architecture, fog nodes collaborate to handle client requests, enabling them to be fulfilled within the fog layer, thereby reducing the load on the cloud and improving overall efficiency. Moreover, to enhance communication security, we employ ciphertext policy attribute-based encryption (CP-ABE), which introduces a novel secure algorithm. The proposed architecture is further evaluated through simulations using omnet++, with experimental results compared to existing research work based on Distributed Hash Table (DHT) protocols.

Vision Transformers (ViT) have recently brought a new wave of research in the field of computer vision. These models have done particularly well in the field of image classification and segmentation. Research on semantic and instance segmentation has emerged to accelerate with the inception of the new architecture, with over 80% of the top 20 benchmarks for the iSAID dataset being either based on the ViT architecture or the attention mechanism behind its success. This paper focuses on the heuristic comparison of three key factors of using (or not using) ViT for semantic segmentation of remote sensing aerial images on the iSAID. The experimental results observed during the course of the research were under the scrutinization of the following objectives: 1. Use of weighted fused loss function for the maximum mean Intersection over Union (mIoU) score, Dice score, and minimization or conservation of entropy or class representation, 2. Comparison of transfer learning on Meta’s MaskFormer, a ViT-based semantic segmentation model, against generic UNet Convolutional Neural networks (CNNs) judged over mIoU, Dice scores, training efficiency, and inference time, and 3. What do we lose for what we gain? i.e., the comparison of the two models against current state-of-art segmentation models. We show the use of the novel combined weighted loss function significantly boosts the CNN model’s performance capacities as compared to transfer learning the ViT. The code for this implementation can be found on https://github.com/ashimdahal/ViT-vs-CNN-Image- Segmentation.