1,721,103 research outputs found
Deep Learning-Powered Computer Vision System for Selective Disassembly of Waste Printed Circuit Boards
No full textThe rapid growth of WPCBs poses significant environmental and resource challenges for their treatment due to their complex composition, which includes approximately 50 CRMs such as copper, beryllium and gallium. While WPCBs represent an environmental problem for their disposal, they also represent an important opportunity because their CRMs content is very high, even compared to that found in naturally occurring mines. Today, however, the recovery of high density CRMs from a circular economy perspective, through automated disassembly WPCBs have the potential because crushing and chemical treatment, is limited, for economic reasons, to a few CRMs (usually gold, copper, palladium and silver), resulting in the loss of the other CRMs.
In order to make the extraction of CRMs from WPCBs cost-effective, this research proposes a new circular economy framework, exploiting the latest machine learning and computer vision techniques, aimed at selectively disassembling (or selecting on a conveyor belt) different types of electronic components to obtain different material, each with a high concentration of specific CRMs, so as to make chemical treatment aimed at their extraction efficient. The developed AI system combines object recognition models, including the YOLO and Transformer architectures, with a recyclability evaluation framework. The system is designed to detect and classify electronic components, quantify their recyclability based on material composition, and optimize CRMs recovery. The V-PCB dataset, curated and annotated as part of this work, serves as a benchmark for training and evaluating the proposed models. Data collection involved high-resolution imaging of V-PCBs using camera modules integrated with NVIDIA Jetson Nano, ensuring scalability for real-world applications. The methodology employs iterative training and domain adaptation techniques to improve model performance. Multi-stage transfer learning strategies were used to adapt the model to varying real-world conditions, significantly improving component recognition accuracy. In addition, the recyclability assessment integrates material analysis techniques such as XRD, providing a comprehensive assessment of the CRMs recovery potential.
Experimental results demonstrate the superiority of the proposed system over traditional methods, achieving high mAP for component detection and ultimately increased high density CRMs recovery rates. The results highlight the feasibility of an automated, sustainable approach to WPCBs recycling that addresses key gaps in existing methods by focusing on component-level disassembly and reuse. This research makes a significant contribution to the field of e-waste management by providing a scalable and efficient solution for the recovery of CRMs. It aligns with circular economy principles by reducing environmental impact, minimizing waste, and promoting the reuse of functional components. The results have been disseminated at leading conferences and the proposed system is ready to transform industrial recycling workflows, making it a critical step towards sustainable electronics manufacturing
Measuring the Recyclability of Electronic Components to Assist Automatic Disassembly and Sorting Waste Printed Circuit Boards
No full textThe 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
Modeling daily energy use in British homes amidst the electricity market crisis: Insights from smart meter and socio-technical data
No full textThis study examines the factors influencing daily electricity use in UK homes during the electricity market crisis. Using data from smart meters and socio-technical sources, the research identifies key drivers of electricity consumption, such as household size, weather conditions, and appliance use. Results from a sample of British homes, analyzed through linear mixed effects modeling, show that households with more occupants, greater number of adults, higher heating settings, and fewer energy-saving efforts consume more energy. Weather, housing type, and air conditioning also play significant roles. The findings highlight the importance of integrating smart meter data with other sources to develop effective energy-saving strategies and inform targeted conservation policies
Latest Trends in Automatic Glioma Tumor Segmentation and an Improved Convolutional Neural Network based Solution
No full textA Brain tumor is an abnormal cell growth in the brain tissues, these tumors are difficult to treat and severely affect the patient's cognitive ability. Out of all brain tumors, gliomas are the deadliest with the least survival rate. The focus of brain tumor segmentation task is to separate tumor tissue such as edema, tumor core from the healthy tissues i.e. white cells, Cerebrospinal Fluid and gray matter. Manual diagnosis of brain tumors from a large amount of patient's MRI images is a tough and time-taking process. With the advent of new approaches, automatic segmentation processes are becoming more effective and clinically accepted. This paper aims to give a comprehensive review of the most state of the art brain tumor segmentation methods. We have given a brief introduction to the imaging modalities and their usage in brain tumor segmentation task We have discussed the results of the most effective approaches by comparing their Dice Score results. We have also discussed some publicly available brain datasets. Furthermore, we have presented a Novel approach for Glioma tumor segmentation using ResNeXt architecture. Experimental results prove that our framework performs well on the dice score
A Systematic Literature Review on the Implementation and Challenges of Zero Trust Architecture Across Domains
No full textThe Zero Trust Architecture (ZTA) model has emerged as a foundational cybersecurity
paradigm that eliminates implicit trust and enforces continuous verification across users, de-
vices, and networks. This study presents a systematic literature review of 74 peer-reviewed
articles published between 2016 and 2025, spanning domains such as cloud computing
(24 studies), Internet of Things (11), healthcare (7), enterprise and remote work systems (6),
industrial and supply chain networks (5), mobile networks (5), artificial intelligence and
machine learning (5), blockchain (4), big data and edge computing (3), and other emerging
contexts (4). The analysis shows that authentication, authorization, and access control
are the most consistently implemented ZTA components, whereas auditing, orchestration,
and environmental perception remain underexplored. Across domains, the main chal-
lenges include scalability limitations, insufficient lightweight cryptographic solutions for
resource-constrained systems, weak orchestration mechanisms, and limited alignment with
regulatory frameworks such as GDPR and HIPAA. Cross-domain comparisons reveal that
cloud and enterprise systems demonstrate relatively mature implementations, while IoT,
blockchain, and big data deployments face persistent performance and compliance barriers.
Overall, the findings highlight both the progress and the gaps in ZTA adoption, under-
scoring the need for lightweight cryptography, context-aware trust engines, automated
orchestration, and regulatory integration. This review provides a roadmap for advancing
ZTA research and practice, offering implications for researchers, industry practitioners, and
policymakers seeking to enhance cybersecurity resilience
Heatmap Visualization for Deep Learning Analysis of Waste Printed Circuit Boards
No full textWaste Printed Circuit Boards (WPCBs) are complex multi-material assemblies that present challenges for automated recycling and Critical Raw Material (CRMs) recovery. Visualization of the part of the WPCBs need more attention and contain high-level density CRMs is challenging in computer vision based system analysis. In this work, we propose a deep learning-based multi-label classification framework integrated with heatmap visualization for interpretable WPCB analysis. We fine-tuned the ResNet50 model as backbone and applied binary cross entropy for each class on custom multi-label V-PCB dataset converted from YOLO format. For visualization of the specific regions across the WPCBs with an image, we applied Gradient-weighted Class Activation Mapping (Grad-CAM) that generate class-specific activation maps corresponding to high density CRMs contained components. Experiments on a custom curated V-PCBs dataset achieve a micro-averaged F1 score of 97.67%. The proposed system provides accurate classification along with interpretable heatmaps, supporting automating vision-based disassembly methods and recovery processes in e-waste recycling
Automatic Prostate Cancer Grading Using Deep Architectures
No full textProstate cancer is the second most aggressive type of cancer among men aged over 45, and it has a major effect on people's lives. Early diagnosis and grading of prostate cancer from tissue images is necessary. Large scale inter observer reproducibility exists in grading the prostate biopsies. This leads us to move towards a computer based model that can accurately detect and grade the cancerous prostate from non-cancerous one. The paper is focused on deep learning based models to automatically grade the prostate cancer from tissue microarray images. Deep learning models directly learn the features via convolutional layers. Two datasets have been used for implementation of our proposed model, Harvard dataset and Gleason Challenge 2019. Our proposed UNET based architecture is used for training as well as validation and testing. We used four different deep learning models, VGG19, ResNet50, Mobilenetv2 and ResNext50 for our UNET based encoder. With our proposed framework, we have achieved 0.728 and 0.732 average Cohen's kappa with F1 on both datasets respectively. The results show that our proposed UNET based deep learning model shows better performance as compared to other state of the art models
Automated Disassembly of Waste Printed Circuit Boards: The Role of Edge Computing and IoT
Full text linkThe ever-growing volume of global electronic waste (e-waste) poses significant environmental and health challenges. Printed circuit boards (PCBs), which form the core of most electronic devices, contain valuable metals as well as hazardous materials. The efficient disassembly and recycling of e-waste is critical for both economic and environmental sustainability. The traditional manual disassembly methods are time-consuming, labor-intensive, and often hazardous. The integration of edge computing and the Internet of Things (IoT) provides a novel approach to automating the disassembly process, potentially transforming the way e-waste is managed. Automated disassembly of WPCBs involves the use of advanced technologies, specifically edge computing and the IoT, to streamline the recycling process. This strategy aims to improve the efficiency and sustainability of e-waste management by leveraging real-time data analytics and intelligent decision-making at the edge of the network. This paper explores the application of edge computing and the IoT in the automated disassembly of WPCBs, discussing the technological framework, benefits, challenges, and future prospects. The experimental results show that the YOLOv10 model achieves 99.9% average precision (AP), enabling accurate real-time detection of electronic components, which greatly facilitates the automated disassembly process
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