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Berry production is increasing worldwide each year; however, high production leads to labor shortages and an increase in wasted fruit during harvest seasons. This problem opened new research opportunities in computer vision as one main challenge to address is the uncontrolled light conditions in greenhouses and open fields. The high light variations between zones can lead to underexposure of the regions of interest, making it difficult to classify between vegetation, ripe, and unripe blackberries due to their black color. Therefore, the aim of this work is to automate the process of classifying the ripeness stages of blackberries in normal and low-light conditions by exploring the use of image fusion methods to improve the quality of the input image before the inference process. The proposed algorithm adds information from three sources: visible, an improved version of the visible, and a sensor that captures images in the near-infrared spectra, obtaining a mean F1 score of 0.909±0.074 and 0.962±0.028 in underexposed images, without and with model fine-tuning, respectively, which in some cases is an increase of up to 12% in the classification rates. Furthermore, the analysis of the fusion metrics showed that the method could be used in outdoor images to enhance their quality; the weighted fusion helps to improve only underexposed vegetation, improving the contrast of objects in the image without significant changes in saturation and colorfulness.
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Aprendizado Profundo , Rubus , Frutas , Algoritmos , LuzRESUMO
Alcohol abuse is not only responsible for 5.3% of the total deaths in the world but also has a substantial impact on neurological and memory disabilities throughout the population. One extensively studied brain area involved in cognitive functions is the hippocampus. Evidence in several rodent models has shown that ethanol produces cognitive impairment in hippocampal-dependent tasks and that the damage is varied according to the stage of development at which the rodent was exposed to ethanol and the dose. To the authors' knowledge, there is a biomarker for cognitive processes in the hippocampus that remains relatively understudied in association with memory impairment by alcohol administration. This biomarker is called sharp wave-ripples (SWRs) which are synchronous neuronal population events that are well known to be involved in memory consolidation. Methodologies for facilitated or automatic identification of ripples and their analysis have been reported for a wider bandwidth than SWRs. This review is focused on communicating the state of the art about the relationship between alcohol, memory consolidation and ripple activity, as well as the use of the common methodologies to identify SWRs automatically.
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Consolidação da Memória , Hipocampo/fisiologia , Etanol/farmacologia , BiomarcadoresRESUMO
Electromyography (EMG) processing is a fundamental part of medical research. It offers the possibility of developing new devices and techniques for the diagnosis, treatment, care, and rehabilitation of patients, in most cases non-invasively. However, EMG signals are random, non-stationary, and non-linear, making their classification difficult. Due to this, it is of vital importance to define which factors are helpful for the classification process. In order to improve this process, it is possible to apply algorithms capable of identifying which features are most important in the categorization process. Algorithms based on metaheuristic methods have demonstrated an ability to search for suitable subsets of features for optimization problems. Therefore, this work proposes a methodology based on genetic algorithms for feature selection to find the parameter space that offers the slightest classification error in 250 ms signal segments. For classification, a support vector machine is used. For this work, two databases were used, the first corresponding to the right upper extremity and the second formed by movements of the right lower extremity. For both databases, a feature space reduction of over 65% was obtained, with a higher average classification efficiency of 91% for the best subset of parameters. In addition, particle swarm optimization (PSO) was applied based on right upper extremity data, obtaining an 88% average error and a 46% reduction for the best subset of parameters. Finally, a sensitivity analysis was applied to the characteristics selected by PSO and genetic algorithms for the database of the right upper extremity, obtaining that the parameters determined by the genetic algorithms show greater sensitivity for the classification process.
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The aim of this work is a control scheme implementation to deform a nonrigid object in which deformation dynamics are modeled by the finite element method. The deformation of a soft object is highly difficult to model because of its non-linearity, time-dependency, and material-response characteristics. Thus, the control implementation for Differential Drive Mobile Robots (DDMR) to deform an elastic object, is a challenge. The proposed steps to solve it are: Position-control designed over DDMR kinematics. Alignment-control applied for DDMRs orientation. The desired shape of the object is achieved using two contact points as the control nodes. A centralized vision algorithm was employed in each stage to obtain positions. To show the usefulness of the proposed scheme, numerical simulation, and real-time implementation were carried out.
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Breast cancer is the most common malignant tumor that affects women in the United States, Europe, and Mexico. As an adverse effect when performing treatments for this condition, secondary lymphedema associated with breast cancer occurs in some cases. This complication occurs due to the interruption of lymphatic flow in the upper extremities in conjunction with other factors such as radiation, sedentary lifestyle, removal of lymph nodes, damage to lymphatic vessels, and others. This article reviews breast cancer incidence, mortality, and survival patterns, confirming that, specifically, lymphedema has high health, social, and economic impacts. Research demonstrates that it fundamentally affects women at an early age. In approximately a third of the cases, it becomes a chronic disease. Therefore, physical therapy is essential for a better quality of life in patients who survive this disease. Surgeries and manual and pharmacological treatments are the current procedures done to reduce to reduce the alterations suffered by patients with lymphedema; however, the success of the treatments depends on each patient's characteristics. To face this problem, the design of a lymphatic vessel has been proposed to assist the mechanical failure of the damaged lymphatic system. In this work, the design methodology used for the blueprint of the lymphatic vessel is presented, as well as the computer analysis of fluid simulation and the selection of the proposed material, resulting in the production of a micrometric design. In the future, it is expected that a surgeon will be able to implant the design of the vessel to restore lymph flow through the lymphatic system, thus helping to combat lymphedema.
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The interface pressure between the residual limb and prosthetic socket has a significant effect on the amputee's mobility and level of comfort with their prosthesis. This paper presents a socket interface pressure (SIFP) system to compare the interface pressure differences during gait between two different types of prosthetic sockets for a transtibial amputee. The system evaluates the interface pressure in six critical regions of interest (CROI) of the lower limb amputee and identifies the peak pressures during certain moments of the gait cycle. The six sensors were attached to the residual limb in the CROIs before the participant with transtibial amputation donned a prosthetic socket. The interface pressure was monitored and recorded while the participant walked on a treadmill for 10 min at 1.4 m/s. The results show peak pressure differences of almost 0.22 kgf/cm2 between the sockets. It was observed that the peak pressure occurred at 50% of the stance phase of the gait cycle. This SIFP system may be used by prosthetists, physical therapists, amputation care centers, and researchers, as well as government and private regulators requiring comparison and evaluation of prosthetic components, components under development, and testing.
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Cotos de Amputação , Membros Artificiais , Desenho de Prótese , Amputação Cirúrgica , Marcha , Humanos , Desempenho Físico Funcional , Tíbia/cirurgiaRESUMO
BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin groups, entities driven by different oncogenic pathways with different clinical outcomes. DLBCL classification by immunohistochemistry (IHC)-based decision tree algorithms is a simpler reported technique than gene expression profiling (GEP). There is a significant discrepancy between IHC-decision tree algorithms when they are compared to GEP. METHODS: To address these inconsistencies, we applied the machine learning approach considering the same combinations of antibodies as in IHC-decision tree algorithms. Immunohistochemistry data from a public DLBCL database was used to perform comparisons among IHC-decision tree algorithms, and the machine learning structures based on Bayesian, Bayesian simple, Naïve Bayesian, artificial neural networks, and support vector machine to show the best diagnostic model. We implemented the linear discriminant analysis over the complete database, detecting a higher influence of BCL6 antibody for GCB classification and MUM1 for non-GCB classification. RESULTS: The classifier with the highest metrics was the four antibody-based Perfecto-Villela (PV) algorithm with 0.94 accuracy, 0.93 specificity, and 0.95 sensitivity, with a perfect agreement with GEP (κ = 0.88, P < 0.001). After training, a sample of 49 Mexican-mestizo DLBCL patient data was classified by COO for the first time in a testing trial. CONCLUSIONS: Harnessing all the available immunohistochemical data without reliance on the order of examination or cut-off value, we conclude that our PV machine learning algorithm outperforms Hans and other IHC-decision tree algorithms currently in use and represents an affordable and time-saving alternative for DLBCL cell-of-origin identification.
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Algoritmos , Perfilação da Expressão Gênica , Centro Germinativo/patologia , Linfoma Difuso de Grandes Células B/classificação , Linfoma Difuso de Grandes Células B/patologia , Aprendizado de Máquina , Adulto , Idoso , Idoso de 80 Anos ou mais , Linfócitos B/patologia , Teorema de Bayes , Árvores de Decisões , Análise Discriminante , Feminino , Perfilação da Expressão Gênica/métodos , Perfilação da Expressão Gênica/estatística & dados numéricos , Humanos , Imuno-Histoquímica/métodos , Imuno-Histoquímica/estatística & dados numéricos , Linfoma Difuso de Grandes Células B/genética , Linfoma Difuso de Grandes Células B/metabolismo , Masculino , Pessoa de Meia-IdadeRESUMO
Deception is considered a psychological process by which one individual deliberately attempts to convince another person to accept as true what the liar knows to be false. This paper presents the use of functional near-infrared spectroscopy for deception detection. This technique measures hemodynamic variations in the cortical regions induced by neural activations. The experimental setup involved a mock theft paradigm with ten subjects, where the subjects responded to a set of questions, with each of their answers belonging to one of three categories: Induced Lies, Induced Truths, and Non-Induced responses. The relative changes of the hemodynamic activity in the subject's prefrontal cortex were recorded during the experiment. From this data, the changes in blood volume were derived and represented as false color topograms. Finally, a human evaluator used these topograms as a guide to classify each answer into one of the three categories. His performance was compared with that of a support vector machine (SVM) classifier in terms of accuracy, specificity, and sensitivity. The human evaluator achieved an accuracy of 84.33 % in a tri-class problem and 92 % in a bi-class problem (induced vs. non-induced responses). In comparison, the SVM classifier correctly classified 95.63 % of the answers in a tri-class problem using cross-validation for the selection of the best features. These results suggest a tradeoff between accuracy and computational burden. In other words, it is possible for an interviewer to classify each response by only looking at the topogram of the hemodynamic activity, but at the cost of reduced prediction accuracy.