The paper concerns the hype idea of “Citizen Science” and the related paradigm shift: to go from the passive “volunteer computing” to other volunteer actions like “volunteer measurements” under guidance of scientists. They can be carried out by ordinary people with standard computing gadgets (smartphone, tablet, etc.) and the various standard sensors in them. Here the special attention is paid to the system of volunteer scientific measurements to study air showers caused by cosmic rays. The technical implementation is based on integration of data about registered night flashes (by radiometric software) in shielded camera chip, synchronized time and GPS-data in ordinary gadgets: to identify night “air showers” of elementary particles; to analyze the frequency and to map the distribution of “air showers” in the densely populated cities. The project currently includes the students of the National Technical University of Ukraine “KPI”, which are compactly located in Kyiv city and contribute their volunteer measurements. The technology would be very effective for other applications also, especially if it will be automated (e.g., on the basis of XtremWeb or/and BOINC technologies for distributed computing) and used in some small area with many volunteers, e.g. in local communities (Corporative/Community Crowd Computing).

Deep learning applications for Edge Intelligence (EI) face challenges in achieving high model performance while maintaining computational efficiency, particularly under varying image orientations and perspectives. This study investigates the synergy of multi-backbone (MB) configurations and Synchronized Multi Augmentation (SMA) to address these challenges by leveraging diverse input representations and spatial transformations. SMA employs synchronously augmented input data across MBs during training, thereby improving feature extraction across diverse representations. The outputs provided by these MBs are merged through different fusion strategies: Averaging Fusion with aggregation of predictions and Dense Fusion with integration of features via a fully connected neural network. It aims to increase model accuracy on previously unseen input data and to reduce computational requirements by minimizing neural network size, particularly advantageous for EI systems characterized by the limited computing resources. This study employed MBs with the MobileNetV3 architecture and the CIFAR-10 dataset to investigate the impact of SMA techniques and different fusion strategies on model robustness and performance. SMA techniques were applied to simulate diverse image orientations, and MB architectures were tested with Averaging and Dense fusion strategies to assess their ability to learn diverse feature representations and improve robustness. The experiments revealed that models augmented with SMA outperformed the baseline MobileNetV3 on modified datasets, achieving higher robustness to orientation variations. Models with Averaging fusion exhibited the most stable performance across datasets, while Dense fusion achieved the highest metrics under specific conditions. Results indicate that SMAs incorporating image transformation adjustments, such as rotation, significantly enhance generalization across varying orientation conditions. This approach enables the production of more stable results using the same pretrained weights in real-world applications by configuring Image Signal Processing (ISP) to effectively use SMA. The findings encourage further exploration of SMA techniques in conjunction with diverse camera sensor configurations and ISP settings to optimize real-world deployments.

This study introduces a novel technique called Synchronized Multi-Augmentation (SMA) combined with multi-backbone (MB) ensembling to enhance model performance and generalization in deep learning (DL) tasks in real-world scenarios. SMA utilizes synchronously augmented input data for training across multiple backbones, improving the overall feature extraction process. The outputs from these backbones are fused using two distinct strategies: the averaging fusion method, which averages predictions, and the dense fusion method, which averages features through a fully connected network. These methods aim to boost accuracy and reduce computational costs, particularly in Edge Intelligence (EI) systems with limited resources. The proposed SMA technique was evaluated on the CIFAR-10 dataset, highlighting its potential to enhance classification tasks in DL workflows. This study provides a comprehensive analysis of various backbones, their ensemble methods, and the impact of different SMAs on model performance. The results demonstrate that SMAs involving color adjustments, such as contrast and equalization, significantly improve generalization under varied lighting conditions that simulated real-world low-illumination conditions, outperforming traditional spatial augmentations. This approach is particularly beneficial for EI hardware, such as microcontrollers and IoT devices, which operate under strict constraints like limited processing power and memory and real-time processing requirements. This study’s findings suggest that employing SMA and MB ensembling can offer significant improvements in accuracy, generalization, and efficiency, making it a viable solution for deploying DL models on edge devices with constrained resources under real-world practical conditions.