Electrical and Electronic Engineering - Research Publications
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ItemDemonstration of Spatial Modulation Using a Novel Active Transmitter Detection Scheme with Signal Space Diversity in Optical Wireless Communications(MDPI, 2022-11-01)Line-of-sight (LOS) indoor optical wireless communications (OWC) enable a high data rate transmission while potentially suffering from optical channel obstructions. Additional LOS links using diversity techniques can tackle the received signal performance degradation, where channel gains often differ in multiple LOS channels. In this paper, a novel active transmitter detection scheme in spatial modulation (SM) is proposed to be incorporated with signal space diversity (SSD) technique to enable an increased OWC system throughput with an improved bit-error-rate (BER). This transmitter detection scheme is composed of a signal pre-distortion technique at the transmitter and a power-based statistical detection method at the receiver, which can address the problem of power-based transmitter detection in SM using carrierless amplitude and phase modulation waveforms with numerous signal levels. Experimental results show that, with the proposed transmitter detection scheme, SSD can be effectively provided with ~0.61 dB signal-to-noise-ratio (SNR) improvement. Additionally, an improved data rate ~7.5 Gbit/s is expected due to effective transmitter detection in SM. The SSD performances at different constellation rotation angles and under different channel gain distributions are also investigated, respectively. The proposed scheme provides a practical solution to implement power-based SM and thus aids the SSD realization for improving system performance.
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ItemImplementation of marker training exercises to improve marking reliability and consistency(Australasian Association for Engineering Education, 2018)CONTEXT: One of the challenges present in teaching a large engineering subject is that of achieving marking consistency of assessments across multiple markers. Several measures of standardising markers exist, such as calibrated review, and are commonly used in the humanities, particularly for assessments that could be prone to a wide variation in marks such as essays. The application of such methods, in an engineering context, is somewhat less documented but of particular importance in the case of reflective writing. This study contrasts the implementation of several different methods of using marker training exercises prior to the actual assessment marking and provides an analysis of the results in order to minimise the effect of multiple marker irregularities and to provide effective high-quality formative feedback on a piece of reflective writing. PURPOSE: This paper presents several different methods of marker training exercises, run prior to the actual assessment marking, and provides analyses to determine the effect of each in terms of minimising marking inconsistency among multiple markers on a piece of reflective writing. APPROACH: In all three marker training exercises, markers are given samples of a piece of reflective writing, of differing quality, along with a rubric outlining the marking criteria for the piece of writing and exemplars for indicative marking standards. Each of the methods employed differ in how the reference standard was set and how feedback was delivered to the markers. Statistics comparing the marking results across markers from before the introduction of the training exercises and between each of the three training methods were analysed to investigate marking reliability and consistency. RESULTS: A significant reduction in the spread of the marker means has been achieved through the introduction of the marker training, indicating an improvement in consistency. Some differences in results between the alternative methods employed has also been observed. CONCLUSIONS: Marking consistency can be improved with the introduction of a marker training exercise prior to the actual assessment marking. Different methods of implementing the marking training exercise, and how the feedback is provided, can have an effect of the amount of improvement in terms of consistency and reliability.
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ItemImplementing a suite of skills modules in a first-year engineering project-based subject(SEFI, 2022)Students commencing engineering at university often have no prior experience of engineering, what the profession entails and the distinctions between disciplines. Consequently, tertiary institutions often offer common first-year engineering subjects that aim to give students an experience of the engineering profession and method, while providing exposure to different disciplines through applied project-based learning. The difficulty with this approach is that there is a wide variance in terms of students’ knowledge, skills, past experiences, and expectations. In a team context, these discrepancies could lead to conflict and poor educational outcomes. Furthermore, if the project is of reasonable length, students might be locked into a discipline-focused project that they realise does not suit them as a potential major. To this end, a suite of self-enrolled skills modules was developed to support student skills development in a first-year, project-based engineering subject under two categories – technical, focusing on skills that had direct applicability to the project-work and general, focusing on skills related to assessment. The modules aimed to (1) improve individuals’ skills in a team context; (2) give students an opportunity to learn skills unrelated to their chosen project in a low-stakes context; and (3) promote interaction and peer-learning outside of their project team and class, building a wider sense of cohort. This paper discusses the creation of the modules and evaluates their outcomes in achieving their goals based on numerous data gathered throughout the semester and student feedback. Initial results have been positive and suggest future directions for development.
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ItemFast Rate Generalization Error Bounds: Variations on a Theme(IEEE, 2022)A recent line of works, initiated by [1] and [2], has shown that the generalization error of a learning algorithm can be upper bounded by information measures. In most of the relevant works, the convergence rate of the expected generalization error is in the form of O(\sqrt λ I/n ) where λ is an assumption-dependent coefficient and I is some information-Theoretic quantities such as the mutual information between the data sample and the learned hypothesis. However, such a learning rate is typically considered to be "slow", compared to a "fast rate"of O(1 /n) in many learning scenarios. In this work, we first show that the square root does not necessarily imply a slow rate, and a fast rate result can still be obtained using this bound by evaluating λ under an appropriate assumption. Furthermore, we identify the key conditions needed for the fast rate generalization error, which we call the ( η, c)-central condition. Under this condition, we give information-Theoretic bounds on the generalization error and excess risk, with a convergence rate of O (1 /n) for specific learning algorithms such as empirical risk minimization. Finally, analytical examples are given to show the effectiveness of the bounds.
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ItemNo Preview AvailableAn Information-Theoretic Analysis for Transfer Learning: Error Bounds and Applications( 2022-07-12)Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different probability distributions. In this work, we give an information-theoretic analysis on the generalization error and excess risk of transfer learning algorithms, following a line of work initiated by Russo and Xu. Our results suggest, perhaps as expected, that the Kullback-Leibler (KL) divergence D(μ||μ′) plays an important role in the characterizations where μ and μ′ denote the distribution of the training data and the testing test, respectively. Specifically, we provide generalization error upper bounds for the empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase. We further apply the analysis to approximated ERM methods such as the Gibbs algorithm and the stochastic gradient descent method. We then generalize the mutual information bound with ϕ-divergence and Wasserstein distance. These generalizations lead to tighter bounds and can handle the case when μ is not absolutely continuous with respect to μ′. Furthermore, we apply a new set of techniques to obtain an alternative upper bound which gives a fast (and optimal) learning rate for some learning problems. Finally, inspired by the derived bounds, we propose the InfoBoost algorithm in which the importance weights for source and target data are adjusted adaptively in accordance to information measures. The empirical results show the effectiveness of the proposed algorithm.
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ItemVaccinia-Virus-Based Vaccines Are Expected to Elicit Highly Cross-Reactive Immunity to the 2022 Monkeypox Virus(MDPI, 2022-09-01)Beginning in May 2022, a novel cluster of monkeypox virus infections was detected in humans. This virus has spread rapidly to non-endemic countries, sparking global concern. Specific vaccines based on the vaccinia virus (VACV) have demonstrated high efficacy against monkeypox viruses in the past and are considered an important outbreak control measure. Viruses observed in the current outbreak carry distinct genetic variations that have the potential to affect vaccine-induced immune recognition. Here, by investigating genetic variation with respect to orthologous immunogenic vaccinia-virus proteins, we report data that anticipates immune responses induced by VACV-based vaccines, including the currently available MVA-BN and ACAM2000 vaccines, to remain highly cross-reactive against the newly observed monkeypox viruses.
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ItemSoft Pneumatic Actuators: A Review of Design, Fabrication, Modeling, Sensing, Control and Applications(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-01-01)
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ItemInferring Epistasis from Genetic Time-series Data(OXFORD UNIV PRESS, 2022-10-07)Epistasis refers to fitness or functional effects of mutations that depend on the sequence background in which these mutations arise. Epistasis is prevalent in nature, including populations of viruses, bacteria, and cancers, and can contribute to the evolution of drug resistance and immune escape. However, it is difficult to directly estimate epistatic effects from sampled observations of a population. At present, there are very few methods that can disentangle the effects of selection (including epistasis), mutation, recombination, genetic drift, and genetic linkage in evolving populations. Here we develop a method to infer epistasis, along with the fitness effects of individual mutations, from observed evolutionary histories. Simulations show that we can accurately infer pairwise epistatic interactions provided that there is sufficient genetic diversity in the data. Our method also allows us to identify which fitness parameters can be reliably inferred from a particular data set and which ones are unidentifiable. Our approach therefore allows for the inference of more complex models of selection from time-series genetic data, while also quantifying uncertainty in the inferred parameters.
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ItemBayesian Detection of a Sinusoidal Signal With Randomly Varying Frequency(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-01-01)
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ItemBeyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management.(JMIR Publications Inc., 2022-06-21)Face masks are an important way to combat the COVID-19 pandemic. However, the prolonged pandemic has revealed confounding problems with the current face masks, including not only the spread of the disease but also concurrent psychological, social, and economic complications. As face masks have been worn for a long time, people have been interested in expanding the purpose of masks from protection to comfort and health, leading to the release of various "smart" mask products around the world. To envision how the smart masks will be extended, this paper reviewed 25 smart masks (12 from commercial products and 13 from academic prototypes) that emerged after the pandemic. While most smart masks presented in the market focus on resolving problems with user breathing discomfort, which arise from prolonged use, academic prototypes were designed for not only sensing COVID-19 but also general health monitoring aspects. Further, we investigated several specific sensors that can be incorporated into the mask for expanding biophysical features. On a larger scale, we discussed the architecture and possible applications with the help of connected smart masks. Namely, beyond a personal sensing application, a group or community sensing application may share an aggregate version of information with the broader population. In addition, this kind of collaborative sensing will also address the challenges of individual sensing, such as reliability and coverage. Lastly, we identified possible service application fields and further considerations for actual use. Along with daily-life health monitoring, smart masks may function as a general respiratory health tool for sports training, in an emergency room or ambulatory setting, as protection for industry workers and firefighters, and for soldier safety and survivability. For further considerations, we investigated design aspects in terms of sensor reliability and reproducibility, ergonomic design for user acceptance, and privacy-aware data-handling. Overall, we aim to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart masks as one of the promising wearable devices. By integrating biomarkers of respiration symptoms, a smart mask can be a truly cutting-edge device that expands further knowledge on health monitoring to reach the next level of wearables.