Subband decomposition techniques for adaptive channel equalisation

In this contribution, the convergence behaviour of the adaptive linear equaliser based on subband decomposition technique is investigated. Two different subband-based linear equalisers are employed, with the aim of improving the equaliser's convergence performance. Simulation results over three channel models having different spectral characteristic are presented. Computer simulations indicate that subband-based equalisers outperform the conventional fullband linear equaliser when channel exhibit severe spectral dynamic. Convergence rate of subband equalisers are governed by the slowest subband, whereby different convergence behaviour in each individual subband is observed. Finally, the complexity of fullband and subband equalisers is discussed

A performance comparison of fullband and different subband adaptive equalisers

We present two different fractionally spaced (FS) equalisers based on subband methods, with the aim of reducing the computational complexity and increasing the convergence rate of a standard fullband FS equaliser. This is achieved by operating in decimated subbands; at a considerably lower update rate and by exploiting the prewhitening effect that a filter bank has on the considerable spectral dynamics of a signal received through a severely distorting channel. The two presented subband structures differ in their level of realising the feedforward and feedback part of the equaliser in the subband domain, with distinct impacts on the updating. Simulation results pinpoint the faster convergence at lower cost for the proposed subband equalisers

Pro-environmentalists behavioural assessment towards energy conservation among Malaysian university students

Pro-environmental are the key players in promoting environmental behaviour to the public. This behaviour covers several broader aspects, especially including energy-used behaviour. However, some career pro-environments ignore their behaviour towards the environment which has affected the public expectation towards them especially in terms of energy conservation. The past researcher believed that pro-environmental literacy should be taught from young ages, at least before they become teenagers or before they get a job placement in future. Therefore, this research was conducted on selected university students in Malaysia. Their knowledge and readiness for pro-environmental behaviour towards energy conservation are really important to ensure the sustainability of development, organization, and workplace. The objectives of this research are to identify the key drivers and pro-environmental barriers among students and to analyse the level of the influences of key drivers and barriers to pro-environmental behaviour levels. The quantitative method was used through the questionnaire with 102 sheets distributed to the students. Descriptive statistic and correlation analysis were used for analysing the data. Each student possesses all six of these categories. But what differentiates them is the level of their application in each category to the dependent variables. The results of the study found that the two categories (activist and avoider) are not too burdensome about energy-used behaviour compared to environmental behaviour. Furthermore, this study found that avoider is the least burdensome category of any behaviour while it is included in the PEB category

Distributed power control and beamforming for cognitive two-way relay networks using a game-theoretic approach

This paper studies a cognitive two-way relay network in which multiple pairs of secondary users (SUs) exchange information with the help of multiple relays. We propose a distributed power control and beamforming algorithm that enables the users operating in the underlay mode to strategically adapt their power levels, and maximize their own utilities subject to the primary user (PU) interference constraint, as well as its own resource and target signal-to-interference-and-noise-ratio (SINR) constraints. The strategic competition among multiple decision makers is modeled as a non-cooperative game where each secondary user (SU) acts selfishly in the sense of maximizing its own utility. An adaptive method is proposed to determine appropriate pricing function. The problem of beamforming optimization under amplify-and-forward (AF) protocol is addressed as a generalized eigen value problem with respect to the utility function of SUs. The existence of a unique Nash equilibrium (NE) is proved and several numerical simulations are conducted to quantify the effect of various system parameters on the performance of the proposed method

Technology of crack detection in reinforced concrete structures

Some crucial signs of structural failure that are critical for repair would be cracks on the structures as well as constant exposure that can result in severe environmental damage. Being able to detect cracks on structures is becoming an essential aspect of the technology of the construction industry. Destructive Testing and Non-Destructive Testing are the two methods used for structural crack detection. This study focused on the techniques used to detect cracks. Several effective methods to detect cracks were carried out and compared to identify the most suitable method in detecting cracks on structures within the demographics of Malaysia. Image processing techniques (IPTs) through the photogrammetry method, surface crack analysis program and Convolution Neural Network (CNN) were carried out to examine crack detection through measurement and monitoring from images. The distance was determined in this study for the physical properties, using both conductibility and accuracy. The photogrammetry method was able to conduct distance at 0.1 - 40 m, with an accuracy of up to 0.005 mm. Therefore, the surface cracks analysis provided 0.10 mm accuracy, while results on CNN had an accuracy of 0.95 mm (98.22 % and 97.95 % in training and validation). Results from physical properties showed that photogrammetry had the highest accuracy, while CNN has the least accuracy. Hence, this study concluded that Photogrammetry method and Convolution Neural Network (CNN) were both the most effective methods to be used in providing clear information and effective ways to detect crack on structures

An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach

SMART: A SpectruM-Aware clusteR-based rouTing scheme for distributed cognitive radio networks

Cognitive radio (CR) is the next-generation wireless communication system that allows unlicensed users (or secondary users, SUs) to exploit the underutilized spectrum (or white spaces) in licensed spectrum while minimizing interference to licensed users (or primary users, PUs). This article proposes a SpectruM-Aware clusteR-based rouTing (SMART) scheme that enables SUs to form clusters in a cognitive radio network (CRN) and enables each SU source node to search for a route to its destination node on the clustered network. An intrinsic characteristic of CRNs is the dynamicity of operating environment in which network conditions (i.e., PUs’ activities) change as time goes by. Based on the network conditions, SMART enables SUs to adjust the number of common channels in a cluster through cluster merging and splitting, and searches for a route on the clustered network using an artificial intelligence approach called reinforcement learning. Simulation results show that SMART selects stable routes and significantly reduces interference to PUs, as well as routing overhead in terms of route discovery frequency, without significant degradation of throughput and end-to-end delay