E-Jurnal Universitas Tunas Husada Tasikmalaya
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Contact-Controlled Thin-Film Transistors and Compact Circuits for Low-Power Sensors and Internet of Things
No full textIn order to deliver billions of cost-effective sensors for Internet of Things applications, large area electronics (LAE) would need to overcome challenges in high-throughput manufacturing methods, while providing power-efficient operation. Processes, such as roll-to-roll and/or inkjet printing, hold the most promise for future disposable and wearable technologies, however progress is limited despite growing demand. The reason is the fundamental electronic device at the heart of their circuits, the thin-film transistor (TFT). TFTs share the same device physics as silicon field-effect transistors found in chip technology, as well as their limitations, notably uniformity of operation. Ordinarily, device non-idealities are compensated through cascode circuits or gain stages, which increases circuit complexity. But these strategies reduce yield, as circuit failure increases with component count. Even though there have been many breakthroughs in material systems, the limitations have persisted. Thus, the ability to produce high yield in high-throughput methods requires TFTs with more robust and uniform operation that can tolerate imprecise processes.An alternative, the source-gated transistor (SGT) is a type of TFT that uses energy barriers at the source contact to control charge injection. As a contact-controlled architecture, the SGT provides uniform and robust operation with extremely high gain and power-efficient operation.In this thesis, SGT operation is further explored in light of off-state behaviour, which produces lower leakage current. The first source-gated transistor (SGT) circuits are also included and provide exemplary performance without support circuitry. Two-transistor (2T) circuits with polysilicon SGTs demonstrate: 49 dB gain in common-source amplifiers, a record for any polysilicon TFT-based amplifier; and current mirrors that have a tuneable temperature dependence by design of the source region, where positive, neutral or negative dependence of output current can be obtained.The SGT’s ability to provide superior high-gain and power-efficient performance in a compact footprint is only superseded by that of the newly invented multimodal transistor (MMT), which shares its benefits. The MMT is an evolution of the contact-controlled concept, where charge injection is separately controlled from channel conduction. As the channel in the MMT is not responsible for charge injection, it provides: faster digital switching (up to two orders-of-magnitude faster than SGTs and one order faster than regular TFTs); an alternative means of charge transport control to mitigate hot-carrier effects in high mobility materials, when doping strategies are unavailable; a unique sample-and-hold or enable line function, ensuring signal propagation when activated. Together with the inherent ability for the MMT to produce a directly proportional dependence of output current on input voltage, these functional benefits allow for extremely compact digital-to-analog conversion and multiplication. Examples of the MMT’s ability to reduce circuit complexity would allow for circuit designers to explore new avenues into developing future LAE applications
Restoring the dignity of indigenous people: Perspectives on tourism employment
No full textThe importance of dignity in tourism employment and the positive impact of Indigenous tourism activities are increasingly acknowledged. Nevertheless, the dignity and well-being of Indigenous people in urban tourism workplaces have received limited attention. Drawing on Indigenous Mexicans as a case study, we use cross-disciplinary concepts of dignity and humanistic management to address this gap by developing recommendations for restoring dignity to Indigenous groups through tourism employment in urban destinations. We explore how tourism employment has resulted in violations of the dignity of Indigenous peoples and illustrate how changes in employment practices across economic, sociocultural, and psychological dimensions can contribute to dignity restoration. By doing so, we advance a conceptual understanding of dignity and guide its practical implementation in tourism employment and management, and policy. Finally, we argue that dignity-restoring practices may also result in improved company performance and reputation as well as contribute to the sustainable development goals.•Poverty, climate change, and other pressures have forced Indigenous people migrate to cities in search of work opportunities•In urban areas, many Indigenous people are trapped in precarious forms of work that violate their dignity•Changes in tourism employment practices can contribute to dignity restoration of Indigenous people•Dignity-restoring employment practices can improve organizational performance while contributing to sustainable development goal
Process vs. outcome: Effects of food photo types in online restaurant reviews on consumers’ purchase intention
No full textUser-generated photos are an important aspect of online reviews; yet they remain fragmented in academic research. This study attempts to investigate the roles of two types of food photos (process-focused and outcome-focused photos) in online restaurant reviews on consumers’ purchase intention, the associated underlying mechanism and the moderating effect of dining motivation based on mental simulation and process transparency theories. Using two experiments, this study revealed that online reviews with process-focused (vs. outcome-focused) food photos led to stronger purchase intention due to higher perceived food quality and experiential value. Moreover, hedonically motivated participants exhibited higher purchase intention towards online reviews with process-focused food photos owing to greater perceived experiential value. Participants with utilitarian motivations expressed stronger purchase intention towards online reviews with outcome-focused food photos due to better perceived food quality. This study provides theoretical insight for the online review literature and practical implications for restaurants’ digital marketing and service design.•Examines the role of food photo type in online reviews on purchase intention.•Adopts an experimental design.•Process- (vs. outcome-) focused food photos exerted a higher purchase intention.•This effect is induced by the higher perceived food quality and experiential value.•This effect is moderated by dining motivation: hedonic vs. utilitarian motivations
Localisation and Optimal Mitigation of Sampling Error in Ensemble Data Assimilation
No full textEnsemble methods are widely used in data assimilation for numerical weather prediction. These methods utilize sample covariance matrices that are subject to sampling error, which is commonly addressed by application of a localisation. The form of the localisation is usually ad-hoc. This thesis develops a series of theoretically optimal localisations based on statistics of the state sampling processes. The theoretical localisations are: (a) Optimal localisation for a Single True Covariance (OSTC); (b) Optimal localisation for a Variable True Covariance (OVTC) which includes knowledge of the climatology; and (c) Hybrid Optimal localisation for a Variable True Covariance (HOVTC) which damps the difference from the mean gain/covariance as opposed to the gain/covariance itself. The optimal localisations are computed to optimise the direct localisation of either the background error covariance or the Kalman gain for assimilating a single observation. The performances of the theoretical localisations are compared to a tuned Gaussian localisation in a series of ideal scenarios where the sampling processes that generate the states are known.The scenarios used to test the localisation include; a Gaussian shaped covariance model and three scenarios of increasing complexity based on 1D geostrophic balance. Localisation can introduce additional imbalance into the analysis state so investigations explore the impact of the localisations on the balance.Results have shown that the theoretical localisations perform comparably to or better than the Gaussian localisation for single observation assimilation but break down for dense observations. HOVTC localisation is shown to outperform traditional forms of localisation in the single observation cases. HOVTC localisation introduces less imbalance than OVTC localisation. It is shown that neither directly optimising the gain nor the covariance are ideal as it is desirable to optimise the gain whilst applying localisation in the numerator and denominator of the gain. In the Gaussian model experiments, a tuned hybrid localisation is proposed based on the form of the optimal hybrid localisation and this is shown to perform well in all ranges of observation density and assimilation strengths.The thesis explores the factors that affect the form and performance of the optimal localisation. It shows that theoretically derived localisations can produce improved assimilation performance for a range of observation densities and assimilation strengths. Finally, it shows that studying the optimal localisation can inform the improvement of localisation regimes for more complex models
Insights on Guerbet reaction: production of biobutanol from bioethanol over a Mg-Al spinel catalyst
No full textSustainable routes for acetic acid production: Traditional processes vs a low-carbon, biogas-based strategy
No full textThe conversion of biogas, mainly formed of CO2 and CH4, into high-value platform chemicals is increasing attention in a context of low-carbon societies. In this new paradigm, acetic acid (AA) is deemed as an interesting product for the chemical industry. Herein we present a fresh overview of the current manufacturing approaches, compared to potential low-carbon alternatives. The use of biogas as primary feedstock to produce acetic acid is an auspicious alternative, representing a step-ahead on carbon-neutral industrial processes. Within the spirit of a circular economy, we propose and analyse a new BIO-strategy with two noteworthy pathways to potentially lower the environmental impact. The generation of syngas via dry reforming (DRM) combined with CO2 utilisation offers a way to produce acetic acid in a two-step approach (BIO-Indirect route), replacing the conventional, petroleum-derived steam reforming process. The most recent advances on catalyst design and technology are discussed. On the other hand, the BIO-Direct route offers a ground-breaking, atom-efficient way to directly generate acetic acid from biogas. Nevertheless, due to thermodynamic restrictions, the use of plasma technology is needed to directly produce acetic acid. This very promising approach is still in an early stage. Particularly, progress in catalyst design is mandatory to enable low-carbon routes for acetic acid production.[Display omitted]•Biogas conversion to acetic acid represents a circular economy route for chemicals manufacturing.•Two new BIO-strategies are proposed to obtain acetic acid from CO2 and CH4.•The implementation of plasma technology in dry reforming represents a step-ahead on carbon-neutral processes.•The state-of-the-art of lab-scale non-thermal plasma dry reforming to value-added products has been reviewed
UAV-enabled Edge Computing for Optimal Task Distribution in Target Tracking
No full text—Unmanned aerial vehicles (UAVs) are useful devices due to their great manoeuvrability for long-range outdoor target tracking. However, these tracking tasks can lead to sub-optimal performance due to high computation requirements and power constraints. To cope with these challenges, we design a UAV-based target tracking algorithm where computationally intensive tasks are offloaded to Edge Computing (EC) servers. We perform joint optimization by considering the trade-off between transmission energy consumption and execution time to determine optimal edge nodes for task processing and reliable tracking. The simulation results demonstrate the superiority of the proposed UAV-based target tracking on the predefined trajectory over several existing techniques. Index Terms—Edge computing (EC), task offloading, un-manned aerial vehicle (UAV
Visually Assisted Self-supervised Audio Speaker Localization and Tracking
No full text—Training a robust tracker of objects (such as vehicles and people) using audio and visual information often needs a large amount of labelled data, which is difficult to obtain as manual annotation is expensive and time-consuming. The natural synchronization of the audio and visual modalities enables the object tracker to be trained in a self-supervised manner. In this work, we propose to localize an audio source (i.e., speaker) using a teacher-student paradigm, where the visual network teaches the audio network by knowledge distillation to localize speakers. The introduction of multi-task learning, by training the audio network to perform source localization and semantic segmentation jointly, further improves the model performance. Experimental results show that the audio localization network can learn from visual information and achieve competitive tracking performance as compared to the baseline methods that are based on the audio-only measurements. The proposed method can provide more reliable measurements for tracking than the traditional sound source localization methods, and the generated audio features aid in visual tracking
Where have all the sound changes gone? Examining the scarcity of evidence for regular sound change in Australian languages
No full textAlmost universally, diachronic sound patterns of languages reveal evidence of both regular and irregular sound changes, yet an exception may be the languages of Australia. Here we discuss a long-observed and striking characteristic of diachronic sound patterns in Australian languages, namely the scarcity of evidence they present for regular sound change. Since the regularity assumption is fundamental to the comparative method, Australian languages pose an interesting challenge for linguistic theory. We examine the situation from two different angles. We identify potential explanations for the lack of evidence of regular sound change, reasoning from the nature of synchronic Australian phonologies; and we emphasise how this unusual characteristic of Australian languages may demand new methods of evaluating evidence for diachronic relatedness and new thinking about the nature of intergenerational transmission. We refer the reader also to Bowern (this volume) for additional viewpoints from which the Australian conundrum can be approached
Noisy Web Supervision for Audio Classification
No full text"Audio classification and other fields of pattern recognition have developed at an astounding pace due to advances in machine learning. The availability of training data, especially labelled training data, remains an important factor for pushing the boundaries further. For this reason, there has been interest in utilising the massive amounts of annotated audio data on the web, which can be retrieved and labelled using automated procedures. Due to labelling errors (label noise) invariably being present, dataset curators have traditionally verified the data and labels manually. This has limited the amount of labelled data available, as manual verification is expensive. Motivated by a desire to remove this cost barrier, this thesis investigates training audio classifiers despite the presence of label noise. This work focuses on learning with web data in particular and in the context of training deep neural networks.To study the effects of real-world label noise, experiments based on synthetic label noise are generally not appropriate. On the other hand, existing audio datasets do not facilitate running controlled experiments, which limits the analysis significantly. To address this, the first contribution of this thesis is a novel audio dataset called ARCA23K, which contains over 23k labelled audio clips sourced from the web. Listening tests are carried out to characterise the label noise present in ARCA23K, revealing that most incorrectly-labelled audio clips are out-of-vocabulary (OOV). A wide array of experiments are conducted to study the impact of label noise on conventional neural network architectures and their learned representations.After studying the effects of label noise on conventional neural networks, a compelling question is whether these effects can be mitigated. To this end, the second contribution is the development of a pseudolabelling algorithm that automatically relabels training examples believed to be labelled incorrectly. While previous work in this area has only considered psuedolabelling for in-vocabulary training examples, the work presented in this thesis specifically argues that learning with OOV examples can be beneficial if the examples are labelled appropriately. The proposed method uses confidence estimation for a data-driven approach to generating labels. Experiments are carried out to confirm the hypothesis and demonstrate the proposed method's superiority to other pseudolabelling methods.Finally, the third contribution is the proposal of a multi-task learning method for learning with noisy labels. More specifically, two tasks are formulated: one task is associated with clean examples and another task is associated with noisy examples. By separating clean and noisy examples in this manner, the effects of label noise can be isolated while also exploiting the overlap between the two tasks via joint training. The proposed multi-task learning approach introduces multiple components to enhance the performance further, including a regularisation technique based on consistency learning and the incorporation of pseudolabelling. A key advantage of the proposed method is its resilience to overfitting on the noisy labels. Using this approach, it is shown that state-of-the-art performance can be attained.