Ant Colony Optimization for Resistor Color Code Detection

In the early stages of learning resistors, introducing color-based values is needed. Moreover, some combinations require a resistor trip analysis to identify. Unfortunately, a resistor body color is considered a local solution, which often confuses resistor coloration. Ant Colony Optimization (ACO) is a heuristic algorithm that can recognize problems with traveling a group of ants. ACO is proposed to select commercial matrix values to be computed without preventing local solutions. In this study, each explores the matrix based on pheromones and heuristic information to generate local solutions. Global solutions are selected based on their high degree of similarity with other local solutions. The first stage of testing focuses on exploring variations of parameter values. Applying the best parameters resulted in 85% accuracy and 43 seconds for 20 resistor images. This method is expected to prevent local solutions without wasteful computation of the matrix

On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBs

Pre-print of an original work presented at KES-IDT 2021 held virtually.We present a dataset consisting of high-resolution images of 13 micro-PCBs captured in various rotations and perspectives relative to the camera, with each sample labeled for PCB type, rotation category, and perspective categories. We then present the design and results of experimentation on combinations of rotations and perspectives used during training and the resulting impact on test accuracy. We then show when and how well data augmentation techniques are capable of simulating rotations vs. perspectives not present in the training data. We perform all experiments using CNNs with and without homogeneous vector capsules (HVCs) and investigate and show the capsules' ability to better encode the equivariance of the sub-components of the micro-PCBs. The results of our experiments lead us to conclude that training a neural network equipped with HVCs, capable of modeling equivariance among sub-components, coupled with training on a diversity of perspectives, achieves the greatest classification accuracy on micro-PCB data

Design and test of readout electronics for medical and astrophysics applications

The applied particle physics has a strong R&D tradition aimed at rising the instrumentation performances to achieve relevant results for the scientific community. The know-how achieved in developing particle detectors can be applied to apparently divergent fields like hadrontherapy and cosmic ray detection. A proof of this fact is presented in this doctoral thesis, where the results coming from three different projects are discussed in likewise macro-chapters. A brief introduction (Chapter 1) reports the basic features characterizing a typical particle detector system. This section is developed following the data transmission path: from the sensor, the data moves through the front-end electronics for being readout and collected, ready for the data manipulation. After this general section, the thesis describes the results achieved in two projects developed by the collaboration between the medical physics group of the University of Turin and the Turin section of the Italian Nuclear Institute for Nuclear Physics. Chapter 2 focuses on the TERA09 project. TERA09 is a 64 channels customized chip that has been realized to equip the front-end readout electronics for the new generation of beam monitor chambers for particle therapy applications. In this field, the trend in the accelerators development is moving toward compact solutions providing high-intensity pulsed-beams. However, such a high intensity will saturate the present readout electronics. In order to overcome this critical issue, the TERA09 chip is able to cope with the expected maximum intensity while keeping high resolution by working on a wide conversion-linearity zone which extends from hundreds of pA to hundreds of μA. The chip gain spread is in the order of 1-3% (r.m.s.), with a 200 fC charge resolution. The thesis author took part in the chip design and fully characterized the device. The same group is currently working on behalf of the MoVeIT collaboration for the development of a new silicon strip detector prototype for particle therapy applications. Chapter 3 presents the technical aspects of this project, focusing on the author’s contribution: the front-end electronics design. The sensor adopted for the MoVeIT project is based on 50 μm thin sensors with internal gain, aiming to detect the single beam particle thus counting their number up to 109 cm2/s fluxes, with a pileup probability < 1%. A similar approach would lead to a drastic step forward if compared to the classical and widely used monitoring system based on gas ionization chambers. For what concerns the front-end electronics, the group strategy has been to design two prototypes of custom front-end: one based on a transimpedance preamplifier with a resistive feedback and the other one based on a charge sensitive amplifier. The challenging tasks for the electronics are represented by the charge and dynamic range which are respectively the 3 - 150 fC and the hundreds of MHz instantaneous rate (100 MHz as the milestone, up to 250 MHz ideally). Chapter 4 is a report on the trigger logic development for the Mini-EUSO detector. Mini-EUSO is a telescope designed by the JEM-EUSO Collaboration to map the Earth in the UV range from the vantage point of the International Space Station (ISS), in low Earth orbit. This approach will lay the groundwork for the detection of Extreme Energy Cosmic Rays (EECRs) from space. Due to its 2.5 μs time resolution, Mini-EUSO is capable of detecting a wide range of UV phenomena in the Earth’s atmosphere. In order to maximize the scientific return of the mission, it is necessary to implement a multi-level trigger logic for data selection over different timescales. This logic is key to the success of the mission and thus must be thoroughly tested and carefully integrated into the data processing system prior to the launch. The author took part in the trigger integration in hardware, laboratory trigger tests and also developed the firmware of the trigger ancillary blocks. Chapter 5 closes this doctoral thesis, with a dedicated summary part for each of the three macro-chapters

Technical Design Report for the PANDA Micro Vertex Detector

This document illustrates the technical layout and the expected performance of the Micro Vertex Detector (MVD) of the PANDA experiment. The MVD will detect charged particles as close as possible to the interaction zone. Design criteria and the optimisation process as well as the technical solutions chosen are discussed and the results of this process are subjected to extensive Monte Carlo physics studies. The route towards realisation of the detector is outlined

Automating Fault Detection and Quality Control in PCBs: A Machine Learning Approach to Handle Imbalanced Data

Printed Circuit Boards (PCBs) are fundamental to the operation of a wide array of electronic devices, from consumer electronics to sophisticated industrial machinery. Given this pivotal role, quality control and fault detection are especially significant, as they are essential for ensuring the devices' long-term reliability and efficiency. To address this, the thesis explores advancements in fault detection and quality control methods for PCBs, with a focus on Machine Learning (ML) and Deep Learning (DL) techniques. The study begins with an in-depth review of traditional approaches like visual and X-ray inspections, then delves into modern, data-driven methods, such as automated anomaly detection in PCB manufacturing using tabular datasets. The core of the thesis is divided into three specific tasks: firstly, applying ML and DL models for anomaly detection in PCBs, particularly focusing on solder-pasting issues and the challenges posed by imbalanced datasets; secondly, predicting human inspection labels through specially designed tabular models like TabNet; and thirdly, implementing multi-classification methods to automate repair labeling on PCBs. The study is structured to offer a comprehensive view, beginning with background information, followed by the methodology and results of each task, and concluding with a summary and directions for future research. Through this systematic approach, the research not only provides new insights into the capabilities and limitations of existing fault detection techniques but also sets the stage for more intelligent and efficient systems in PCB manufacturing and quality control

Homogeneous vector capsules and their application to sufficient and complete data

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCapsules (vector-valued neurons) have recently become a more active area of research in neural networks. However, existing formulations have several drawbacks including the large number of trainable parameters that they require as well as the reliance on routing mechanisms between layers of capsules. The primary aim of this project is to demonstrate the benefits of a new formulation of capsules called Homogeneous Vector Capsules (HVCs) that overcome these drawbacks. Using HVCs, new state-of-the-art accuracies for the MNIST dataset are established for multiple individual models as well as multiple ensembles. This work additionally presents a dataset consisting of high-resolution images of 13 micro-PCBs captured in various rotations and perspectives relative to the camera, with each sample labeled for PCB type, rotation category, and perspective categories. Experiments performed and elucidated in this work examine classification accuracy of rotations and perspectives that were not trained on as well as the ability to artificially generate missing rotations and perspectives during training. The results of these experiments include showing that using HVCs is superior to using fully connected layers. This work also showed that certain training samples are more informative of class membership than others. These samples can be identified prior to training by analyzing their position in reduced dimensional space relative to the classes’ centroids in that space. And a definition and calculation both for class density and dataset completeness based on the distribution of data in the reduced dimensional space has been put forth. Experimentation using the dataset completeness calculation shows that those datasets that meet a certain completeness threshold can be trained on a subset of the total dataset, based on each class’s density, while improving upon or maintaining validation accuracy

End-of-life implications of electronic textiles - Assessment of a converging technology

Contemporary innovation in the converging technology sectors of electronics and textile aims at augmenting functionality of textiles, making them “smart”. That is, integrating electronic functions such as sensing, data processing, and networking into wearable products. Embedding electronic devices into textiles results in a novel category of products: electronic textiles (e-textiles). Whereas researchers and innovators are pushing forward technological development little attention has been paid to the end-of-life implications of such future products. E-textiles may not only entail promising business opportunities but also adverse environmental impacts. This study examines potential end-of-life implications, which could emerge once future e-textiles are disposed of. Using the methodological framework of technology assessment an overview of current innovation processes for e-textiles is established and an outlook on future applications areas is provided. Further, information on technologies and materials composition of e-textiles is mapped as a basis for assessing the prospective implications at the end of their useful life. The findings suggest that widespread application of e-textiles could result in the emergence of a new waste stream. There are various parallels to electronic waste, which causes profound environmental problems nowadays. Risks include potential release of toxic substances during the disposal phase. And, loss of scarce materials is to be expected if no recycling takes place. This would accelerate the depletion of resources. Recycling of textile integrated electronic devices will be difficult. From the analysis it can be deduced that today’s schemes for takeback, recycling and disposal would not be sufficient to cope with waste e-textiles in an environmentally benign manner. Instead, discarded e-textiles would find their way into solid waste and increase the existing environmental problems of waste disposal. The study concludes with recommendations for policy makers and technology developers on how a waste preventative technology design could be achieved