This paper presents a groundbreaking advancement in industrial quality control through the development of an automated soldering quality assessment system for circuit boards utilizing hyperspectral imaging (USI) technology. Building upon the transformative capabilities of USI in visual inspection, our research focuses on enhancing the precision and depth of assessment in soldering processes, a critical aspect of electronics manufacturing. By leveraging the unique spectral information captured by HSI, beyond the capabilities of traditional vision systems, our automated solution offers a comprehensive evaluation of solder quality, overcoming challenges posed by similar absorption characteristics of materials. We detail the methodology, algorithms, and integration of HSI into the inspection pipeline, highlighting its effectiveness in detecting defects, ensuring uniformity, and improving overall product quality. The application of this technology extends beyond electronics manufacturing, with potential implications for various industries requiring meticulous quality control. Through this study, we contribute to the ongoing evolution of visual inspection systems, empowering industries with advanced tools for precise and reliable quality assessment.

This paper presents two novel feature representations for neuromorphic audio data. Neuromorphic audio data are considered state-of-the-art when precise time responses are needed while also keeping energy-demands to a minimum. The approaches presented here are based on the concept of phased encoding of neuromorphic data to generate feature representations. One of the approaches enhances on this further by utilising an autoencoder to reduce the dimensionality of the feature representation allowing for increased accuracy in noise-rich environments such as industrial shop floors. The approaches are evaluated against other leading audio feature representation methods using a neuromorphic version of the TIDIGITS database and results demonstrate high accuracy for the proposed approach. We also find that the autoencoder-backed method achieves the best performance compared with the other methods as the dimensionality reduction results in a generalised representation of the feature set which is less sensitive when compared to other methods.

This paper explores the application of BlazePose, a monocular human pose estimation (HPE) model, within a teleoperation framework for a UR5 six-axis robot. Achieving teleoperation with only a single RGB camera and a device without a powerful GPU will improve accessibility and cost effectiveness of teleoperation solutions. This study evaluates the 2D pose estimation capabilities of BlazePose for robotic teleoperation tasks. Given the necessity of manipulating the UR5 in three- dimensional space, we implement a 2D-based controller that translates the teleoperator's 2D right hand position within a configurable hand workspace to the corresponding position of the robot's Tool Centre Point (TCP) within the robot's available workspace along two dimensions. The left hand is then utilised for controlling the robot's motion along the third dimension and operating the attached OnRobot RG2 gripper during the pick- and-place task. Additionally, we explore an alternative control paradigm utilising the 3D pose estimation of BlazePose for a more intuitive controller. Two experiments are conducted: the pick-and-place task to assess the 2D-based controller in common robotic tasks, and a hold position task. The hold position task aims to assess the amount of excess movement attributable to the HPE model when utilising the 3D-based controller. The results reveal that while the 2D pose estimation capabilities enable effective teleoperation, the utilisation of 3D estimation results in poor translation to robot control and significant excess motion. These findings underscore the importance of accurate depth estimation in 3D HPE models for precise and reliable teleoperation.

Neural coding schemes are powerful tools used within neuroscience. This paper introduces three different neural coding scheme formations for event-based vision data which are designed to emulate the neural behaviour exhibited by neurons under stimuli. Presented are phase-of-firing and two sparse neural coding schemes. It is determined that machine learning approaches, i.e. Convolutional Neural Network combined with a Stacked Autoencoder network, produce powerful descriptors of the patterns within events. These coding schemes are deployed in an existing action recognition template and evaluated using two popular event-based data sets.

Event-based image processing is a relatively new domain in the field of computer vision. Much research has been carried out on adapting event-based data to comply with established techniques from frame-based computer vision. On the contrary, this paper presents a descriptor which is designed specifically for direct use with event-based data and therefore can be considered to be a pure event-based vision descriptor as it only uses events emitted from event-based vision devices without transforming the data to accommodate frame-based vision techniques. This novel descriptor is known as the Post-stimulus Time-dependent Event Descriptor (P-TED). P-TED is comprised of two features extracted from event data which describe motion and the underlying pattern of transmission respectively. Furthermore a framework is presented which leverages the P-TED descriptor to classify motions within event data. This framework is compared against another state-of-the-art event-based vision descriptor as well as an established frame-based approach.

This paper presents a novel Reducing-Over-Time (ROT) binary tree structure for event-based vision data and subtypes of the tree structure. A framework is presented using ROT, that takes advantage of the self-balancing and self-pruning nature of the tree structure to extract spatial-temporal information. The ROT framework is paired with an established motion classification technique and performance is evaluated against other state-of-the-art techniques using four datasets. Additionally, the ROT framework as a processing platform is compared with other event-based vision processing platforms in terms of memory usage and is found to be one of the most memory efficient platforms available.