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Recent Submissions

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Time-Interleaved noise jamming

(2022-12) Claasen, Gert; Du Plessis, Warren P

Modern jamming systems are faced with the reality that multiple threat radars will be encountered simultaneously, and potentially, all simultaneously encountered threat radars will need to be countered to prevent detection. Modern jammer systems are capable of extremely rapid reconfiguration, allowing them to counter multiple simultaneous threats in a time-interleaved manner. However, simultaneously encountered threats cause jamming pulses to coincide in time, leading to the problem of deciding which of the threats to jam when such coincidences occur. In this article, an approach to determining the relative priority of jammer pulses is proposed and evaluated to show that time-interleaved jamming is a viable approach to effectively countering multiple simultaneous threats.

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Advanced production and security

(2026-08) Koen, Hildegarde S

The rapid integration of AI copilots into productivity and professional software platforms, including Microsoft 365, integrated development environments (IDEs), Adobe applications, and engineering tools, is transforming the way researchers and knowledge workers perform their tasks. These technologies offer substantial gains in efficiency, creativity, and productivity by assisting with content generation, coding, data analysis, and decision-making processes. However, the increasing adoption of AI-enabled solutions introduces significant information security and privacy concerns, particularly when cloud-based processing is involved. Sensitive organisational data may be inadvertently exposed through user prompts, uploaded documents, or AI-generated outputs. As institutions seek to remain innovative and competitive, they must also implement robust governance, security controls, and user awareness measures to protect confidential information. Achieving an appropriate balance between technological advancement and information protection remains a critical challenge in the responsible deployment of AI copilots.

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LCBE AI assessment & opportunity framework

(2026-08) CSIR

This presentation assesses the current state of artificial intelligence (AI) adoption across the six departments within the LCBE portfolio and identifies opportunities for advancing AI-enabled transformation. The findings indicate that AI use remains largely informal and fragmented, with most applications focused on report writing, document drafting, meeting summaries, data analysis, and productivity enhancement. Despite varying levels of maturity, all departments share a common ambition to transition from reactive and periodic processes toward integrated, predictive, and intelligence-driven operations. Potential applications include risk intelligence, customer engagement, operational monitoring, compliance management, legal decision support, facilities management, and business resilience analytics. The assessment highlights key barriers to adoption, including data quality challenges, system integration limitations, governance requirements, confidentiality concerns, unequal access to AI tools, and skills gaps among staff. The study concludes that successful AI implementation requires a coordinated enablement strategy encompassing people, data, technology, governance, and continuous human oversight to ensure responsible, secure, and scalable deployment across the portfolio.

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AI in ICT: What's real today, and what it takes to go further

(2026-08) CSIR

This presentation examines the current state of artificial intelligence (AI) adoption within information and communication technology (ICT) environments and outlines the requirements for scaling AI-driven innovation. It presents a structured approach comprising vendor-provided AI solutions, business-built AI applications, and ICT-engineered AI systems, supported by an enabling layer focused on adoption, governance, and value realization. The discussion highlights practical AI use cases already delivering benefits, including software development acceleration, operational efficiency, document processing, intelligent chatbots, workflow automation, and productivity enhancement through AI copilots. A bursary screening case study demonstrates how AI can support document analysis while maintaining human oversight and decision-making authority. The presentation further emphasizes the critical success factors for sustainable AI implementation, including leadership alignment, data readiness, enterprise governance, responsible AI practices, workforce capability development, and effective change management. It concludes that realizing the full potential of AI in ICT requires balancing technological innovation with security, ethical considerations, and organizational readiness.

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A blockchain-based bimodal voter accreditation system (Block-BVAS): A framework for adoption in electronic voting systems

(2026-08) Ohize, HO; Onumanyi, Adeiza James; Ajao, LA; Umar, BU; Ijalija, JM; Olosunde, SA; Abdullahi, AO; Olaniyi, OM

Despite significant advances in electronic voting technologies, voter accreditation in many electoral systems remains vulnerable to identity fraud, database tampering, equipment failure, and centralized security breaches. Existing accreditation solutions often rely on single-modal biometric authentication and centralized architectures, limiting their robustness, transparency, and public trust. This paper proposes a Blockchain-based Bimodal Voter Accreditation System (Block-BVAS), together with a practical framework for its deployment in electronic voting systems. The proposed system integrates multimodal biometric authentication using facial and fingerprint recognition with a private Ethereum blockchain and conventional cryptographic mechanisms to provide secure, tamper-resistant, and auditable voter accreditation to provide secure, decentralized, and tamper-resistant voter accreditation. A Raspberry Pi 5 serves as the embedded processing platform, demonstrating the feasibility of implementing the framework on cost-effective hardware. By combining distributed-ledger technology with encrypted biometric verification, the proposed archiAcademicEditor: MaurizioTalamo Received: 27April2026 Revised: 20August2026 Accepted: 21August2026 Published: 27 August2026 Copyright: ©2026bytheauthors. Licensee MDPI,Basel,Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY)license. tecture enhances the integrity, confidentiality, and immutability of election-related records while addressing limitations associated with single-factor authentication and conventional centralized record management. Experimental evaluation of the biometric authentication module performed effectively, with fingerprint recognition achieving an average authentication accuracy (AA) of 97.8% and facial recognition averaging 95.1%. The blockchain storage overhead (BSO) displayed a near-linear growth pattern relative to the number of transactions, consistent with theoretical expectations for blockchain architectures. Reliability analysis indicated system uptime exceeding 95%, with only minimal operational failures recorded during the test period. This blockchain implementation further demonstrated reliable transaction processing and secure record management, indicating the effectiveness of the proposed Block-BVAS in enhancing the security, transparency, and trustworthiness of electronic voter accreditation.