The procedure of processing the vermicompost production includes several stages, where the vermicompost material has different temperatures during these different stages. Thermal sensors play a key role in numerous fields, such as medical and agricultural applications. Thermal cameras can produce a thermal image or an array of values representing the array of sensory data. i.e., an array of temperatures. In this study, we proposed the first thermal imagery dataset of the vermicompost production process. The contributions of this work are two-fold using the proposed dataset. First, we framed the process of predicting the vermicompost production process as a classification problem. Second, we compared classifying the different stages of the process of vermicompost production based on two different input types, namely, thermal images and an array of temperatures. In other words, the classifier will be fed with an input (an image or an array of temperatures), and then the classifier will predict the vermicompost production stage. In this context, we utilized several machine and deep learning models as classifiers. For the utilized dataset, the study has been conducted on a set of images collected during the vermicompost production procedure which was collected every 14 days over 42 consecutive days, i.e., four classes.We proposed running a series of experiments to determine which input type yields better classification accuracy. The obtained results show that using thermal images for the sake of classifying the vermicompost production stages achieved higher accuracy, about 92%, in comparison to using the sensor array data, about 60%.

Human Pose Estimation (HPE) models have varied applications and represent a cutting-edge branch of study, whose systems such as MediaPipe (MP), OpenPose (OP), and AlphaPose (ALP) show marked success. One of these areas, however, that is inadequately researched is the impact of image degradation on the accuracy of HPE models. Image degradation refers to images whose visual quality has been purposefully degraded by means of techniques, such as brightness adjustments (which can lead to an increase or a decrease in the intensity levels), geometric rotations, or resolution downscaling. The study of how these types of degradation impact the performance functionality of HPE models is an under-researched domaina that is a virtually unexplored area. In addition, current methods of the efficacy of existing image restoration techniques have not been rigorously evaluated and improving degraded images to a high quality has not been well examined in relation to improving HPE models. In this study, we explicitly clearly demonstrate a decline in the precision of the HPE model when image quality is degraded. Our qualitative and quantitative measurements identify a wide difference in performance in identifying landmarks as images undergo changes in brightness, rotation, or reductions in resolution. Additionally, we have tested a variety of existing image enhancement methods in an attempt to enhance their capability in restoring low-quality images, hence supporting improved functionality of HPE. Interestingly, for rotated images, using Pillow of OpenCV improves landmark recognition precision drastically, nearly restoring it to levels we see in high-quality images. In instances of brightness variation and in low-quality images, however, existing methods of enhancement fail to yield the improvements anticipated, highlighting a large direction of study that warrants further investigation and calls for additional research. In this regard, we proposed a wide-ranging system for classifying different types of image degradation systematically and for selecting appropriate algorithms for image restoration, in an effort to restore image quality. A key finding is that in a related study of current methods, the Tuned RotNet model achieves 92.04% accuracy, significantly outperforming the baseline model and surpassing the official RotNet model in predicting rotation degree of images, where the accuracy of official RotNet and Tuned RotNet classifiers were 61.59% and 92.04%, respectively. Furthermore, in an effort to facilitate future research and make it easier for other studies, we provide a new dataset of reference images and corresponding degenerated images, addressing a notable gap in controlled comparative studies, since currently there is a lack of controlled comparatives.

American scientific publishing group

Nowadays, multi-core processors are increasingly adopted in embedded systems. These processors can achieve energy consumption minimization by employing dynamic voltage/frequency scaling techniques (DVFS). Several energy-aware real-time task partitioning algorithms have been suggested for multicore processors. While many of these algorithms focus on independent real-time tasks, there has been relatively limited research dedicated to task synchronization. This paper focuses on optimizing energy consumption by assigning dependent real-time tasks to a multi-core processor. When multiple tasks on different cores access shared resources simultaneously, it can result in longer blocking times, consequently increasing the execution time of tasks. This situation can result in missing hard deadlines, potentially causing system failure. The Highest Task-Based Partitioning (HTBP) algorithm is structured to decrease overall energy consumption while ensuring deadlines are met. It allocates tasks with high similarity (accessing the same set of resources) to the same core, effectively minimizing the occurrence of remote blockings. In the evaluation of the HTBP algorithm, we compared it with similarity-based partitioning (SBP), worst-fit decreasing (WFD) and best-fit decreasing (BFD). Our results indicate that our proposed (HTBP) algorithm outperforms SBP, WFD, and BFD algorithms (bin-packing algorithms), minimizes the overall energy dissipation, and improves schedulability.