The Raspberry Pi 5 is several times faster than previous models of the compact and cheap computer. For less than a couple hundred bucks, you can have a computer that can do many tasks that previously required a regular PC, that consumes very little power, and for which the web is overflowing with exciting tutorials and projects you can take on. In this guide, I'll first walk you through how to build a Raspberry Pi 5 with maximum performance so you can use it for everything it's capable of. To do that, you'll need to take advantage of the board's new ability to connect an SSD via PCI Express. You will also need a power source, either a USB charger that supports USB-PD and a USB-C cable or the official charger from Raspberry Pi.

The Raspberry Pi 5 is several times faster than previous models of the compact and cheap computer. For less than a couple hundred bucks, you can have a computer that can do many tasks that previously required a regular PC, that consumes very little power, and for which the web is overflowing with exciting tutorials and projects you can take on. In this guide, I'll first walk you through how to build a Raspberry Pi 5 with maximum performance so you can use it for everything it's capable of. To do that, you'll need to take advantage of the board's new ability to connect an SSD via PCI Express. You will also need a power source, either a USB charger that supports USB-PD and a USB-C cable or the official charger from Raspberry Pi.

The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-the-art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes.

While current personal smart devices excel in digital domains, they fall short in assisting users during human environment interaction. This paper proposes Heads Up eXperience (HUX), an AI system designed to bridge this gap, serving as a constant companion across the extended reality (XR) environments. By tracking the user's eye gaze, analyzing the surrounding environment, and interpreting verbal contexts, the system captures and enhances multi-modal data, providing holistic context interpretation and memory storage in real-time task specific situations. This comprehensive approach enables more natural, empathetic and intelligent interactions between the user and HUX AI, paving the path for human computer environment interaction. Intended for deployment in smart glasses and extended reality headsets, HUX AI aims to become a personal and useful AI companion for daily life. By integrating digital assistance with enhanced physical world interactions, this technology has the potential to revolutionize human-AI collaboration in both personal and professional spheres paving the way for the future of personal smart devices.

With the rapid growth of the PCB manufacturing industry, there is an increasing demand for computer vision inspection to detect defects during production. Improving the accuracy and generalization of PCB defect detection models remains a significant challenge. This paper proposes a high-precision, robust, and real-time end-to-end method for PCB defect detection based on deep Convolutional Neural Networks (CNN). Traditional methods often suffer from low accuracy and limited applicability. We propose a novel approach combining YOLOv5 and multiscale modules for hierarchical residual-like connections. In PCB defect detection, noise can confuse the background and small targets. The YOLOv5 model provides a strong foundation with its real-time processing and accurate object detection capabilities. The multi-scale module extends traditional approaches by incorporating hierarchical residual-like connections within a single block, enabling multiscale feature extraction. This plug-and-play module significantly enhances performance by extracting features at multiple scales and levels, which are useful for identifying defects of varying sizes and complexities. Our multi-scale architecture integrates feature extraction, defect localization, and classification into a unified network. Experiments on a large-scale PCB dataset demonstrate significant improvements in precision, recall, and F1-score compared to existing methods. This work advances computer vision inspection for PCB defect detection, providing a reliable solution for high-precision, robust, real-time, and domain-adaptive defect detection in the PCB manufacturing industry.

The waste of electrical and electronic equipment has been increased due to the fast evolution of technology products and competition of many IT sectors. Every year millions of tons of electronic waste are thrown into the environment which causes high consequences for human health. Therefore, it is crucial to control this waste flow using technology, especially using Artificial Intelligence but also reclamation of critical raw materials for new production processes. In this paper, we focused on the measurement of recyclability of waste electronic components (WECs) from waste printed circuit boards (WPCBs) using mathematical innovation model. This innovative approach evaluates both the recyclability and recycling difficulties of WECs, integrating an AI model for improved disassembly and sorting. Assessing the recyclability of individual electronic components present on WPCBs provides insight into the recovery potential of valuable materials and indicates the level of complexity involved in recycling in terms of economic worth and production utility. This novel measurement approach helps AI models in accurately determining the number of classes to be identified and sorted during the automated disassembly of discarded PCBs. It also facilitates the model in iterative training and validation of individual electronic components.

This contribution gives an overview of an ongoing project using machine learning and computer vision components for improving the electronic waste recycling process. In circular economy, the "virtual mines" concept refers to production cycles where interesting raw materials are reclaimed in an efficient and cost-effective manner from end-of-life items. In particular, the growth of e-waste, due to the increasingly shorter life cycle of hi-tech goods, is a global problem. In this paper, we describe a pipeline based on deep learning model to recycle printed circuit boards at the component level. A pre-trained YOLOv5 model is used to analyze the results of the locally developed dataset. With a different distribution of class instances, YOLOv5 managed to achieve satisfactory precision and recall, with the ability to optimize with large component instances.

The disposal and recycling of electronic waste (e-waste) is a global challenge. The disassembly of components is a crucial step towards an efficient recycling process, avoiding the destructive methods. Although most disassembly work is still done manually due to the diversity and complexity of components, there is a growing interest in developing automated methods to improve efficiency and reduce labor costs. This study aims to robotize the desoldering process and extracting components from printed circuit boards (PCBs), with the goal of automating the process as much as possible. The proposed strategy consists of several phases, including the controlled contact of the robotic tool with the PCB components. A specific tool was developed to apply a controlled force against the PCB component, removing it from the board. The results demonstrate that it is feasible to remove the PCB components with a high success rate (approximately 100% for the bigger PCB components).

Xiaolang Zhang, the former Apple employee who pleaded guilty to stealing information about the development of the company's self-driving vehicle, has been sentenced to 120 days in prison followed by a three-year supervised release. Zhang was arrested back in 2018 at San Jose International Airport just as he was about to board a flight to China. He initially pleaded not guilty until he changed his tune in 2022 and admitted to stealing trade secrets. In addition to serving time behind bars, he also has to pay restitution amounting to 146,984, according to the court document of his sentencing first seen by 9to5Mac. Zhang originally faced up to 10 years in prison and a fine of 250,000.

The Raspberry Pi is already versatile out of the box. But with hardware extensions that are plugged directly onto the board, you can significantly increase the possibilities. We introduce you to some remarkable components. With the "Hardware Attached on Top" (HAT) concept, the Raspberry has an easy way to expand its capabilities. The add-ons are simply plugged onto the GPIO pins on the board. The expansion kits usually also include plastic spacers to protect against short circuits if the main board and expansion board come too close together.