Common Spatial Pattern (CSP) is a widely utilized technique in ElectroEncephaloGraphy (EEG)-based Motor Imagery (MI) Brain-Computer Interfaces (BCIs) for feature extraction, owing to its simplicity and effectiveness in improving classification performance. However, traditional CSP methods have limitations, including sensitivity to outliers and prone to overfitting, random variations in accuracy, and a lack of interpretability due to the predefined number of features. This paper introduces a novel framework integrating CSP with Explainable Artificial Intelligence (XAI) techniques to address these limitations. The proposed explainable CSP algorithm enhances classification accuracy while providing insights into feature selection and spatial patterns contributing to predictive accuracy. Key contributions include a mechanism for explainable feature selection, dynamic optimization of the number of features guided by explainability metrics, and validation across different subjects. Results showed that the proposed XAI-CSP method can outperform traditional CSP by 3% and similar CSP variants, such as Filter Bank CSP (FBCSP), by nearly 8% in average classification accuracy.

Retinopathy is a group of retinal disabilities that causes severe visual impairments or complete blindness. Due to the capability of optical coherence tomography to reveal early retinal abnormalities, many researchers have utilized it to develop autonomous retinal screening systems. However, to the best of our knowledge, most of these systems rely only on mathematical features, which might not be helpful to clinicians since they do not encompass the clinical manifestations of screening the underlying diseases. Such clinical manifestations are critically important to be considered within the autonomous screening systems to match the grading of ophthalmologists within the clinical settings. To overcome these limitations, we present a novel framework that exploits the fusion of vision language correlation between the retinal imagery and the set of clinical prompts to recognize the different types of retinal disabilities. The proposed framework is rigorously tested on six public datasets, where, across each dataset, the proposed framework outperformed state-of-the-art methods in various metrics. Moreover, the clinical significance of the proposed framework is also tested under strict blind testing experiments, where the proposed system achieved a statistically significant correlation coefficient of 0.9185 and 0.9529 with the two expert clinicians. These blind test experiments highlight the potential of the proposed framework to be deployed in the real world for accurate screening of retinal diseases.

Brain-computer interface (BCI) technology has great potential in control, communication, and neurological diagnostics by interpreting EEG signals. This paper introduces a novel method for extracting distinguishing features from EEG signals using a Common Spatial Patterns (CSP) model, enhanced with a Phase Space Reconstruction model and followed by an autoregressive (AR) model. Initially, EEG signals are denoised using an IIR Butterworth filter before feature extraction. Two classifiers, Linear Discriminant Analysis (LDA) and Multi-Layer Perceptron (MLP), are employed to differentiate features across four motor imagery (MI) tasks. For a four-class MI task, combining spatial and temporal-frequency domain features, LDA achieves an accuracy of 67.40%, while MLP reaches 64.78%. LDA yields precision and recall scores of 67.40% and 67.20%, respectively, for the F1-score, whereas MLP records precision, recall, and F1-scores of 65.36%, 64.78%, and 64.28%, respectively. The proposed architecture is evaluated using the BCI Competition IV set 2a dataset, proving its effectiveness in EEG signal classification for BCI applications.

Outsourcing chip production is common among semiconductor vendors to cope with the increasing demand for integrated circuits. This has resulted in several security issues in the chip supply chain, including hardware trojans, intellectual property theft, and overproduction. The concept of zero trust –never trust, always verify– presents a promising solution for ensuring the authenticity of Integrated Circuits (ICs), particularly in critical systems where adversary attacks can cause significant losses or damage. The Security Protocol and Data Model (SPDM) is a reliable protocol that uses certificates to ensure the authenticity of ICs. Based on this protocol, the presented paper proposes a chip-to-chip zero-trust security architecture that aims to verify the authenticity of any connected peripheral before its use. The contributions include an overview of the proposed architecture, the anti-clock stretching technique, and an analysis of the challenges encountered during the implementation and execution.

Remote Identification (RID) technology provides a digital license plate for Unmanned Aerial Vehicles (UAVs) to monitor airspace and enforce lawful behavior. RID enables the detection of drones from long distances and the differentiation between legal and illegal operations. This paper proposes a low-power Add-on RID (AoRID) module as part of a comprehensive airspace monitoring system comprised of three components: the AoRID module as the transmitter, a smartphone as the receiver, and an Unmanned Traffic Management (UTM) database. The system offers advantages in its simplicity, effectiveness, and affordability.

Outsourcing chip production is common among semiconductor vendors to cope with the increasing demand for integrated circuits. This has resulted in several security issues in the chip supply chain, including hardware trojans, intellectual property theft, and overproduction. Zero-trust presents a promising solution for ensuring the authenticity of Integrated Circuits (ICs), particularly in critical systems where adversary attacks can cause significant losses or damage. The Security Protocol and Data Model (SPDM) is a reliable protocol that uses certificates to ensure the authenticity of ICs. Based on this protocol, the presented paper proposes a chip-to-chip zero-trust security architecture that aims to verify the authenticity of any connected peripheral before its use. The contributions include an overview of the proposed architecture, implementation and formal verification of the SPDM protocol, and analysis of the challenges encountered during the implementation and execution.

Web access and web resources open many horizons, their usage increases in all life aspects including government, education, commerce and entertainment, where the key to such resources lies in Web browsers. Acknowledging the importance of universal accessibility to web resources, the W3C has developed a series of guidelines into a Web Accessibility Initiative (WAI), with the goal of providing access to web resources for people with disabilities. In order to bridge the gap in the digital divide between the disabled and the non-disabled people, the authors believe that the development of novel assistive technologies using new human-computer interfaces will go a long way towards achieving this lofty goal. In this paper, they present a P300 Electroencephalography Brain-controlled Web browser to enhance the accessibility of people with severe motor disabilities to Web resources. It enhances their interaction with the Web taking their needs into account. The proposed Web browser satisfies the Mankoff's requirements of a system that would “allow true web access.”