Video encoding and decoding tools are now a mainstay of most consumer electronics products, both portable and for home and office use. However, the algorithms that integrate them are complex, require high computational power and, therefore, energy and hardware resources. In this study, we assess the possibility of improving the energy consumption of an embedded VVC decoder by comprehensively reducing its performance. The objective is to find a reliable method to scale down the performance of the decoding devices according to the properties of the video to decode and to a performance objective. We rely on a lightweight machine learning-based algorithm to predict to which point the performance should be reduced. The experimental results show energy savings up to 50%, and recommend moving this framework to working frequency, leaving quasi-static the number of cores.

Hyperspectral imaging analyzed by machine learning algorithms is a powerful tool to classify materials, tissues, molecules and pathogens. By analyzing the electromagnetic spectrum of liquid serum samples, it has been demonstrated that it is possible to predict which patients with possible head trauma injury will have a possible result on computer tomography. This process is being carried out with very complex, slow and expensive spectrometric techniques. To tackle this problem, this study presents a simple hyperspectral imaging system that allows the capture of multiple serum samples with one single scan, without light artifacts as it works in transmittance and without a high data redundancy rate. Throughout this paper, the main characteristics of this system, the preprocessing chain necessary to extract the information from these captures, the working methodology, and the analysis performed are presented. Hyperspectral images of plasma from 405 patients were captured and the signatures obtained from this system were compared with the signatures captured by a spectrometer, which served as a reference system. With a mean correlation of 97.3% and a standard deviation of 3.6%, the presented system not only captures correctly liquid samples, but also provides spatial information and can capture many more samples in a single scan. In addition, a statistical study is presented on which spectrum bands present a higher concentration of information, which will be very beneficial for future analysis.

In the field of hyperspectral imaging, accurate and reliable data analysis is essential for many applications, including medicine, remote sensing, and material science. White calibration is a critical step in this process, as it accounts for deviations in the light source intensity and spectral distribution. Nevertheless, it is important to note that the specific geometry of the scene plays a crucial role in the white calibration, affecting the angle of incidence of the light and, subsequently, the measured spectral response. By taking surface normals and depth information into account during white calibration, we can ensure that subsequent hyperspectral images are accurately calibrated and comparable, leading to more robust and meaningful data analysis. For that matter, in this paper, we demonstrate a methodology to fuse hyperspectral and depth information, and how this fusion can help to correct different geometrical properties of the analyzed sample. A series of laboratory experiments were conducted on samples with different geometries and surface properties. Specifically, the error dispersion of the spectral signatures was reduced from 12 % to less than 4 %, a substantial improvement that highlights the potential of the proposed method.

Hyperspectral (HS) imaging (HSI) techniques have demonstrated to be useful in the medical field to characterize tissues without any contact and without ionizing the patient. Besides, HSI combined with supervised machine learning (ML) algorithms have proven to be an effective technique to assist neurosurgeons to resect brain tumors. This research looks at the effects of hyperparameter optimization on two common supervised ML algorithms used for brain tumor classification: support vector machines (SVM) and random forest (RF). Correctly classifying brain tumor with HS data containing low spatial and spectral information can be challenging. To tackle this problem, this study has applied hyperparameter optimization techniques on SVM and RF with 10 brain images of patients suffering from glioblastoma multiforme (GBM) with non-mutated isocitrate dehydrogenase (IDH) enzymes. These captures have 409x217 spatial resolution and 25 normalized reflectance wavelengths gathered from 665 to 960 nm with a HS snapshot camera. Results show how this work has been able to obtain 98,60% of weighted area under the curve (AUC) on the test score by employing naive optimizations like grid search (GS) or random search (RS) and even more complex methods based on Bayesian optimization (BO). Not only the weighted AUC of SVM has been improved by 8%, but BO have also enhanced the AUC of the tumor class by 22.50% in comparison with non-optimized SVM models in the state-of-the-art, achieving AUC values of 95,49% on the tumor class. Furthermore, these improvements have been illustrated with classification maps to demonstrate the importance of hyperparameter optimization on SVM to clearly classify brain tumor, whereas non-optimized models from previous studies are unable to detect the tumor.