Numerous bats species have evolved biosonar to obtain information from their habitats with dense vegetation. Different from man-made sensors, such as stereo cameras and LiDAR, bats' biosonar has much lower spatial resolution and sampling rates. Their biosonar is capable of reliably finding narrow gaps in foliage to serve as a passageway to fly through. To investigate the sensory information under such capability, we have used a biomimetic sonar robot to collect the narrow gap echoes from an artificial hedge in a laboratory setup and from the natural foliage in outdoor environments respectively. The work in this dissertation presents the performance of a conventional energy approach and a deep-learning approach in the classification of echoes from foliage and gap. The deep-learning approach has better foliage versus passageway classification accuracy than the energy approach in both experiments, and it also shows good robustness than the latter one when dealing with data with great varieties in the outdoor experiments. A class activation mapping approach indicates that the initial rising flank inside the echo spectrogram contains critical information. This result corresponds to the neuromorphic spiking model which could be simplified as times where the echo amplitude crosses a certain threshold in a certain frequency range. With these findings, it could be demonstrated that the sensory information in clutter echoes plays an important role in detecting passageways in foliage regardless of the wider beamwith than the passageway geometry. / Doctor of Philosophy / Many bats species are able to navigate and hunt in habitats with dense vegetation based on trains of biosonar echoes as their primary sources for sensory information on the environment. Drones equipped with man-made sensory systems such as optical, thermal, or LiDAR sensors, still face challenges when navigating in dense foliage. Bats are not only able to achieve higher reliability in detecting narrow gaps but accomplish this with much lower spatial resolutions and data rates than those of man-made sensors. To study which sensory information is accessible to bat biosonar for detecting passageways in foliage, a robot consisting of a biomimetic sonar and a camera system has been used to collect a large number of echoes and corresponding images (∼130k samples) from an artificial hedge constructed in the laboratory and various natural foliage targets found outdoors. We have applied a conventional energy approach which is widely used in engineered sonar but is limited by the biosonar's wide beamwidth and only achieves a foliage-versus-passageway classification accuracy of ∼70%. To deal with this situation, a deep-learning approach has been used to improve performance. Besides that, a transparent AI approach has been applied to overcome the black-box property and highlight the region of interest of the deep-learning classifier. The results achieved in detecting passageways were closely matched between the artificial hedge in the laboratory setup and the field data. With the best classification accuracy of 97.13% (artificial hedge) and 96.64% (field data) by the deep-learning approach, this work indicates the potential of exploring sensory information based on clutter echoes from complex environments for detecting passageways in foliage.

In this work two methods that are widely used to peek into the inner workings of artificial neural networks (ANN) - information theory and perturbation experiments– are compared. Both were applied to a positive control ANN and their results were contrasted. Their results were not complementary as expected. Information theory identified that information is redundant across the hidden nodes while the perturbation experiment found false positives: -nodes with little information and high impact on the output-. Each method by itself seems to be insufficient to understand how the system works and a new method should be developed.

Process monitoring is a field that can greatly benefit from the adoption of machine learning solutions like recommendation systems. However, for domain experts to embrace these technologies within their work processes, clear explanations are crucial. Therefore, it is important to adopt user-centred methods for designing more transparent recommendation systems. This study explores this topic through a case study in the pulp and paper industry. By employing a user-centred and design-first adaptation of the question-driven design process, this study aims to uncover the explanation needs and requirements of industry experts, as well as formulate design visions and recommendations for transparent recommendation systems. The results of the study reveal five common explanation types that are valuable for domain experts while also highlighting limitations in previous studies on explanation types. Additionally, nine requirements are identified and utilised in the creation of a prototype, which domain experts evaluate. The evaluation process leads to the development of several design recommendations that can assist HCI researchers and designers in creating effective, transparent recommendation systems. Overall, this research contributes to the field of HCI by enhancing the understanding of transparent recommendation systems from a user-centred perspective.