Reconfigurable intelligent surfaces (RISs), as ar-tificial electromagnetic structures with programmable electro-magnetic characteristics, have the potential to enhance the signals collected by service base stations in multi-cell Internet of things (IoT) networks. This capability improves the information extraction and utilization in communication systems, presenting numerous promising applications in the sixth-generation (6G) communication systems-IoT network environment. This paper proposes an optimization framework for the transmission sum-rate in a multicell, multi-user wireless communication system assisted by RIS. Firstly, establish transmission power allocation constraints for training and data symbols based on the distances of users from the transmitter, along with corresponding sum-rate objective functions. Utilize the multidimensional feature parameters of the RIS and long short-term memory (LSTM) to establish the relationship between user mobility distance and transmission power. Then, based on the obtained power prediction data, calculate the corresponding sum-rate using the previously defined objective functions. The obtained predictions contribute to more accurately assessing future communication environments, thereby aiding in optimizing the performance of communication systems. Simulation results validate the significant potential of LSTM in RIS-assisted multi-user wireless network systems.

This work proposes the utilization of the quantum approximate optimization algorithm (QAOA) for user pairing in non-orthogonal multiple access (NOMA). By exploiting quantum concepts such as the quantum adiabatic theorem, a user pairing solution was derived that approximates a suboptimal sum rate. Moreover, an improved-QAOA is proposed to further enhance the performance by reducing the number of users in the cluster by dividing it into more clusters. Simulation results have demonstrated that the improved-QAOA yields a higher average achievable sum rate when compared to QAOA, and its solution is closer to the theoretical optimal value obtained by a brute-force enumeration method.

Reconfigurable intelligent surfaces (RISs)-assisted Internet of Things (IoT) networks have attracted a great deal of interest due to the potential contributions to the next generation wireless networks. In this article, an adaptive RIS clustering system with weak channels or obstacles is investigated. Ideally, there is a suitable RIS that adopts the self-organized RIS-assisted (SORA) scheme between a base station (BS) and a user equipment (UE) for reflection transmission. However, due to the nonideal channel scenarios, a single RIS may not be capable of connecting the BS with the UE. To tackle these nonideal cases, we propose a complementary RIS-cluster-assisted (C-RCA) strategy by reflecting signals among clusters to improve the performance of the overall system. The performances of the two strategies in terms of outage probability (OP) and ergodic capacity (EC), derived using approximate closed-form expressions, are analyzed. By increasing the numbers of complementary RISs and the reflecting elements on each RIS simultaneously, these performance metrics as well as the effective throughput and energy efficiency (EE) are evaluated under different signal-to-noise ratios (SNRs). The obtained results demonstrated that, in contrast to the ideal case, the network EE of the proposed C-RCA strategy is higher than the SORA scheme under the larger target spectral efficiency (SE).

The demand for skills in science, technology, engineering and mathematics (STEM) is consistently high worldwide. However, the number of female students studying these subjects at higher education level has been consistently under represented, especially in engineering. In this paper, we share some insights into the reasons why some female engineering students in Malaysia and Kazakhstan have chosen to study engineering, and what they think is preventing more of their female contemporaries from studying engineering.

Capstone project (CP) is an integral part of an undergraduate engineering degree. It is important because it gives students the opportunity to demonstrate the breadth of knowledge and skills that they have learnt in the whole of their undergraduate courses. It is therefore critical to have a well-defined and well-designed CP as a concluding part to a Bachelor degree engineering program especially in demonstrating that students and the program fulfil the accreditation requirements of relevant accreditation bodies. In this paper, we discuss how these CP assessment tasks can be conducted such that they can be mapped to most of the 42 points within the 6 broad categories of the Engineering Council UK (ECUK) graduate competencies requirements.

The panoramic unwrapping of catadioptric omnidirectional view (COV) sensors have mostly relied on a precomputed mapping look-up table due to an expensive computational load that generally has its bottleneck occur at solving a sextic polynomial. However, this approach causes a limitation to the viewpoint dynamics as runtime modifications to the mapping values are not allowed in the implementation. In this paper, a parallel root-finding technique using Compute Unified Device Architecture (CUDA) platform is proposed. The proposed method enables on-the-fly computation of the mapping look-up table thus facilitate in a real-time viewpoint adjustable panoramic unwrapping. Experimental results showed that the proposed implementation incurred minimum computational load, and performed at 10.3 times and 2.3 times the speed of a current generation central processing unit (CPU) respectively on a single-core and multi-core environment.

A reduced complexity multiple-input multiple-output (MIMO) channel estimator known as the polynomial-predictor-based vector generalized least mean squares (VGLMS) estimator is developed. It is a simplification of a previously developed polynomial-predictor-based vector generalized recursive least squares (VGRLS) estimator, achieved by replacing the online recursive computation of the 'intermediate' matrix by an offline pre-computed matrix. Similar to the VGRLS estimator, it is able to operate in Rayleigh or Rician fading environments without reconfiguration of the state transition matrix to accommodate the non-random mean components. It is seen to offer a trade-off between reduced complexity channel estimation and good system performance.