Unified Compressive Sensing Paradigm for the Random Demodulator and Compressive Multiplexer Architectures

A major challenge in spectrum sensing for cognitive radio (CR) applications is the very high sampling rates involved, which imposes significant demands on the signal acquisition technology. This has given impetus to applying compressive sensing (CS) as a sub-Nyquist sampling paradigm for CR-type wireless signals which exhibit sparsity in certain domains. CS architectures like the random demodulator (RD) and compressive multiplexer (CM) can be used for CR spectral sensing, though both are inherently restricted in terms of the signal classes they can effectively process. To address these limitations, this paper presents two unified RD and CM-based CS architectures that seamlessly integrate precolouring and the multitaper spectral estimator into their respective structures to facilitate efficient sensing of both digitally modulated and narrowband signals, along with popular CR-access technologies like orthogonal frequency division multiplexing. A significant feature of these unified CS architectures is they do not require a priori knowledge of either the input signal or modulation scheme, while a tristate spectral classifier is introduced to afford notably enhanced spectrum access opportunities for unlicensed secondary users. A critical performance evaluation corroborates that both unified architectures demonstrate consistently superior CS results and robustness across a broad range of CR-type signals, modulations and access technologies

A Multitaper-Random Demodulator Model for Narrowband Compressive Spectral Estimation

The random demodulator (RD) is a compressive sensing (CS) system for acquiring and recovering bandlimited sparse signals, which are approximated by multi-tones. Signal recovery employs the discrete Fourier transform based periodogram, though due to bias and variance constraints, it is an inconsistent spectral estimator. This paper presents a Multitaper RD (MT-RD) architecture for compressive spectrum estimation, which exploits the inherent advantage of the MT spectral estimation method from the spectral leakage perspective. Experimental results for sparse, narrowband signals corroborate that the MT-RD model enhances sparsity so affording superior CS performance compared with the original RD system in terms of both lower power spectrum leakage and improved input noise robustness

Decoupling security concerns in web services using aspects

This paper discusses the Aspect-oriented Framework for Web services (AoF4WS) that supports on-demand context-sensitive security in Web services. Flexible security schemes are needed in many Web services applications where authentication, authorization, etc., can no longer be used in their current form. Security mechanisms are to be customized to the continuously changing requirements of Web services. Examples of this customization concern cryptographic protocol for a specific situation and timeout for user credentials. The AoF4WS uses aspect-oriented programming and frames. Aspects provide flexibility to the framework, and frames adjust aspects to specific requirements. © 2006 IEEE

Bridging the BAME Divide: Unveiling the Impacts of Covid-19 on Ethnic Minority Students and Empowering Change—A Case Study at the Open University

This study investigates the evolving impact of COVID-19 on the learning experiences and study performance of ethnic minority students enrolled in Level 1 Computing modules at the Open University. A mixed-methods approach combining quantitative data analysis, literature review, and two focus groups was employed to provide fresh insights. Findings from the literature and focus groups highlight persistent challenges faced by ethnic minority students, including economic disadvantage, digital divide, housing instability, employment difficulties, family responsibilities, mental health issues, racism, discrimination, and unconscious bias. Importantly, this study reveals the dynamic nature of these challenges, illustrating how they have evolved throughout the ongoing pandemic. The study underscores the pivotal role of structural and institutional factors in shaping students’ ever-changing experiences. In response to these dynamic challenges, recommendations include targeted interventions, policy revisions that reflect the shifting landscape, innovative community-building initiatives, a renewed focus on diversity promotion, enhanced support services, unconscious bias training, and revised tuition strategies. Addressing these dynamic challenges is crucial for fostering equitable educational opportunities and outcomes for ethnic minority students. This research significantly contributes to promoting equality, inclusivity, and a more comprehensive understanding of the ever-evolving experiences of ethnic minority students during the pandemic and beyond

Context-driven policy enforcement and reconciliation for Web services

Security of Web services is a major factor to their successful integration into critical IT applications. An extensive research in this direction concentrates on low level aspects of security such as message secrecy, data integrity, and authentication. Thus, proposed solutions are mainly built upon the assumption that security mechanisms are static and predefined. However, the dynamic nature of the Internet and the continuously changing environments where Web services operate require innovative and adaptive security solutions. This paper presents our solution for securing Web services based on adaptive policies, where adaptability is satisfied using the contextual information of the Web services. The proposed solution includes a negotiation and reconciliation protocol for security policies

A novel precolouring-random demodulator architecture for compressive spectrum estimation

One of the main challenges of conventional spectrum estimation methods in cognitive radio applications is the very high sampling rates involved, which imposes significant operating demands upon the analog-to-digital converter (ADC). This has given impetus to employing compressive sensing (CS) techniques, such as the random demodulator (RD) structure to relax the input ADC specification. It has been recently shown the RD spectrum estimation performance for quadrature phased shift keying (PSK) modulated signals can be significantly improved in terms of spectral concentration and signal-to-noise ratio, when signals are precoloured by an autoregressive (AR) filter. This paper presents an extended AR-RD architecture, which provides enhanced CS capability for higher-order digital modulation schemes, including 16 quadrature amplitude modulation (16QAM), 64QAM and binary PSK (BPSK). Quantitative results corroborate the improved CS performance of the AR-RD structure for higher-order modulations schemes, which provides a propitious design trade-off between AR-RD complexity, latency and CS performance

»A French Music of France«

Abstract – One of the major challenges in cognitive radio (CR) networks is the need to sample signals as efficiently as possible without incurring the loss of vital information. Compressive Sensing (CS) is a new sampling paradigm which provides a theoretical framework for sub-sampling signals which are characterized as being sparse in the frequency domain. The random demodulator (RD) is a CS-based architecture which has been employed to acquire frequency sparse, bandlimited signals which typify the signals which often occur in many CR-related applications. This paper investigates the impact of precolouring upon CS performance by combining the RD with an autoregressive (AR) filter model to enhance compressive spectral estimation. Quantitative results with quadrature phased shift keying (QPSK) modulated multiband signals, corroborate that adopting a precolouring strategy both reduces the spectral leakage in the power spectrum, and concomitantly improves the overall signal-to-noise ratio (SNR) performance of the compressive spectrum estimator

Joint Proceedings of the Workshops: IWUC, MDEIS and TCoB

This volume contains the joint Proceedings of the 5th International Workshop on Ubiquitous Computing (IWUC-2008), the 4th International Workshop On Model-Driven Enterprise Information Systems (MDEIS-2008) and the 3rd International Workshop on Technologies for Context-Aware Business Process Management (TCoB-2008)