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Thermographic image analysis is a subfield of diagnostic image processing aimed at detecting breast abnormalities in women at an early stage. It is a developing field of research and its effectiveness and scope require scientific assessment to be determined. An open-access dataset has been created for the scientific community to test and develop techniques for computational detection of normal and abnormal breast conditions from thermograms. This dataset is a valuable resource for researchers due to the scarcity of publicly available datasets of breast thermographic images. It includes thermographic images of the female chest area in three capture positions: anterior, left oblique and right oblique. The data set comes from 119 women ranging from 18 to 81 years of age. A table is attached to the dataset with the diagnosis of breast pathology, showing that 84 patients had benign pathology and 35 patients had malignant pathology. The diagnoses of women with healthy breast pathology are not included.

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This article presents a dataset of thermographic images of terrain with antipersonnel mines to identify the presence or absence of these artifacts using machine learning and artificial vision techniques. The dataset has 2700 thermographic images acquired at different heights, using a Zenmuse XT infrared camera (7-13 µm), embedded in the DJI Matrice 100 drone. The data acquisition experiment consists of capturing aerial infrared images of a terrain where elements with characteristics similar to antipersonnel mines type legbreaker were buried. The mines were planted in the ground between 0 cm and 10 cm deep and were spread over an area of 10 m x 10 m. The drone used a flight protocol that set the trajectory, the time of the flight, the acquisition height, and the image sampling frequency. This dataset was used in "Detection of "legbreaker" antipersonnel landmines by analysis of aerial thermographic images of the soil" [7].

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Visual tracking of objects is a fundamental technology for industry 4.0, allowing the integration of digital content and real-world objects. The industrial operation known as manual cargo packing can benefit from the visual tracking of objects. No dataset exists to evaluate the visual tracking algorithms on manual packing scenarios. To close this gap, this article presents 6D-ViCuT, a dataset of images, and 6D pose ground truth of cuboids in a manual packing operation in intralogistics. The initial release of the dataset comprehends 28 sessions acquired in a space that rebuilds a manual packing zone: indoors, area of (6 × 4 × 2) m3, and warehouse illumination. The data acquisition experiment involves capturing images from fixed and mobile RGBD devices and a motion capture system while an operator performs a manual packing operation. Each session contains between 6 and 18 boxes from an available set of 10 types, with each type varying in height, width, depth, and texture. Each session had a duration in the range of 1 to 5 minutes. Each session exhibits operator speed and box type differences (box texture, size heterogeneity, occlusion).

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Pulsed thermography is a nondestructive method commonly used to explore anomalies in composite materials. This paper presents a procedure for the automated detection of defects in thermal images of composite materials obtained with pulsed thermography experiments. The proposed methodology is simple and novel as it is reliable in low-contrast and nonuniform heating conditions and does not require data preprocessing. Nonuniform heating correction and the gradient direction information combined with a local and global segmentation phase are used to analyze carbon fiber-reinforced plastic (CFRP) thermal images with Teflon inserts with different length/depth ratios. Additionally, a comparison between the actual depths and estimated depths of detected defects is performed. The performance of the nonuniform heating correction proposed method is superior to that obtained on the same CFRP sample analyzed with a deep learning algorithm and the background thermal compensation by filtering strategy.

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Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

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The intention of the experiment is to investigate whether different sounds have influence on heart signal features in the situation the observer is judging the different sounds as positive or negative. As the heart is under (para)sympathetic control of the nervous system this experiment could give information about the processing of sound stimuli beyond the conscious processing of the subject. As the nature of the influence on the heart signal is not known these signals are to be analysed with AI/machine learning techniques. Heart rate variability (HRV) is a variable derived from the R-R interval peaks of electrocardiogram which exposes the interplay between the sympathetic and parasympathetic nervous system. In addition to its uses as a diagnostic tool and an active part in the clinic and research domain, the HRV has been used to study the effects of sound and music on the heart response; among others, it was observed that heart rate is higher in response to exciting music compared with tranquilizing music while heart rate variability and its low-frequency and high-frequency power are reduced. Nevertheless, it is still unclear which musical element is related to the observed changes. Thus, this study assesses the effects of harmonic intervals and noise stimuli on the heart response by using machine learning. The results show that noises and harmonic intervals change heart activity in a distinct way; e.g., the ratio between the axis of the ellipse fitted in the Poincaré plot increased between harmonic intervals and noise exposition. Moreover, the frequency content of the stimuli produces different heart responses, both with noise and harmonic intervals. In the case of harmonic intervals, it is also interesting to note how the effect of consonance quality could be found in the heart response.

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This article presents a dataset of raw microscopic images of the prefrontal cortex from wistar rat tissues, after an induced stroke, stained with NeuN antibody. The raw images were captured using a microscope equipped with a digital camera. The dataset is useful for testing techniques for the improvement, registration, and stitching to generate a high-resolution image with a full reconstruction of tissues. Besides, this dataset can be used to assess the neuronal brain after an ischemic event. The dataset contains 1370 microscope images with 20x magnification and 36 (Hierarchical Data Format version 5) hdf5 files with homography matrices between every pair of sequential images per tissue rows.

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The electrocardiogram is traditionally used to diagnose a large number of heart pathologies. Research to improve the readability and classification of cardiac signals includes studies geared toward sonification of the electrocardiographic signal and others involving features related to music processing, such as Mel-frequency cepstral coefficients. In terms of music processing features, this study seeks to use music information retrieval (MIR) features as electrocardiographic signal descriptors. The study compares the discriminatory capability of the introduced features in relation to standard groups such as heart rate variability, wavelet transform, descriptive statistics, Mel coefficients and fractal analysis, evaluated using classification algorithms; the signals analyzed were extracted from public databases. The group of features extracted from wavelet transform and the MIR group showed a high level of discrimination; the best representation of the ECG signals in the study was achieved in most cases by the MIR features. Moreover, a correlation coefficient higher than 0.8 was found between a number of MIR and other feature groups, indicating a likely relationship between the electrocardiographic signals and MIR features. These results suggest the feasibility of representing the analyzed signals by music information retrieval descriptors, giving the potential to consider these electrocardiographic signals as analogues to musical signals.

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PURPOSE: Propose and develop new multimodal interfaces that allow people with motor impairments to control mass use applications in a natural way through gestures and voice. METHODS: A multimodal interaction interface was developed for using Google Chrome, Gmail and Facebook applications through gestural and verbal commands. The interface activates mouse and keyboard commands from the processing of voice signals and videos of the user's head movement. The interface does not disable traditional keyboard and mouse functions; moreover, it only requires a webcam and a microphone, which are usually built into portable computers. RESULTS: The tests were performed on three groups of people: young adults, older adults and people with motor impairments. The verbal interaction was tested on a total of 189 voice commands with an average performance of 75.7% and a total of 105 dictations. The dictations had an average of 13 words; the system had a performance of 81.1%. Moreover, the gestural interaction activated 126 commands without errors using a drop-down menu; a click was activated 84 times with a success rate of 70.2%. CONCLUSION: The motor impairments group especially valued the option of using Google Chrome, Gmail and Facebook without physically manipulating a mouse and keyboard. This group showed a greater preference for verbal control than for gestural control. An adaptation period is required for the adults group to acquire greater skill in using the interface. The young adults group preferred the types of interactions they are accustomed to due to their familiarity with Information and Communication Technologies (ICT); they considered the interface fun.Implication for rehabilitationComputers and their applications were conceived with unnatural interaction mechanisms, such as the keyboard and mouse, which prevent their use by people with psychophysiological limitations or digital literacy. For this reason, the need arises to design new natural interfaces commanded by gestures and voice.It is necessary to develop low-cost interfaces that can control mass-use applications such as Google Chrome, Facebook and Gmail, which do not require additional hardware using webcams and microphones, which are usually integrated into laptops.The development of these multimodal interfaces improves the quality of life of people with motor impairments, allowing them to have access to Information and Communication Technologies (ICT).

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This paper presents a thermal imaging dataset from composite material samples (carbon and glass fiber reinforced plastic) that were inspected by pulsed thermography with the goal of detecting and characterizing subsurface defective zones (Teflon inserts representing delaminations between plies). The pulsed thermography experiment was applied to 6 academic plates (inspected from both sides) all having the dimensions of 300 mm x 300 mm x 2 mm and same distribution of defects but made of different materials: three plates on carbon fiber-reinforced plastic (CFRP) and three plates made on glass fiber reinforced plastic (GFRP) specimens with three different geometries: planar, curved and trapezoidal. Each plate contains 25 inserts having length/depth ratios between 1.7 and 75. Two FX60 BALCAR photographic flashes (6.2 kJ per flash) were used to generate the heat pulse (2 ms duration), an X6900 FLIR infrared camera using ResearchIR software to record the thermal images and a custom-built software/control unit to synchronize data recording with pulse generation. Finally, the dataset proposed consists of 12 sequences of approximately 2000 images of 512 × 512 pixels each.

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This article presents a dataset of thermal and visible aerial images of the same flat scene at Melendez campus of Universidad del Valle, Cali, Colombia. The images were acquired using an UAV equipped with either a thermal or a visible camera. The dataset is useful for testing techniques for the improvement, registration and fusion of multi-modal and multi-spectral images. The dataset consists of 30 visible images and their metadata, 80 thermal images and their metadata, and a visible georeferenced orthoimage. The metadata related to every image contains the WGS84 coordinates for allocating the images. Also, the homography matrices between every image and the orthoimage are included in the dataset. The images and homographies are compatible with the well-known assessment protocol for detection and description proposed by Mikolajczyk and Schmid [1].

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This article presents a dataset for thermal characterization of photovoltaic systems to identify snail trails and hot spot failures. This dataset has 277 thermographic aerial images that were acquired by a Zenmuse XT IR camera (7-13 µ m wavelength) from a DJI Matrice 100 1drone (quadcopter). Additionally, our dataset includes the next environmental measurements: temperature, wind speed, and irradiance. The experimental set up consisted in a photovoltaic array of 4 serial monocrystalline Si panels (string) and an electronic equipment emulating a real load. The conditions for images acquisition were stablished in a flight protocol in which we defined altitude, attitude, and weather conditions.

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Resumen El número de trabajos relacionados con Interfaces Cerebro-Computador (BCI, Brain-Computer Interface en inglés) directamente aplicados al proceso de rehabilitación de pacientes con lesiones de médula espinal está en aumento debido a la mejora en las técnicas de procesamiento digital de señales y reconocimiento de patrones que permiten relacionar las señales electroencefalográficas con acciones motoras. Los resultados preliminares de las pruebas de las BCI sobre sujetos reales permiten visualizar en un futuro relativamente cercano la inclusión de este tipo de herramientas en los protocolos de rehabilitación. Sin embargo, hay muchas barreras por resolver, principalmente las relacionadas con el aumento del desempeño y la generación de múltiples comandos naturales mediante interfaces cerebro-computador a partir de electroencefalografía superficial (EEG). En este trabajo se hace una revisión de los más importantes trabajos que muestran la evolución, el estado actual y las oportunidades de investigación alrededor de la temática de interfaces cerebro-computador en procesos de neurorrehabilitación de miembros superiores en pacientes con lesiones medulares.

Abstract The number of researches related to rehabilitation processes in spinal cord injury patients using Brain-Computer Interfaces is increasing due to the development of improved digital signal processing and pattern recognition techniques that allows decoding motor actions from electroencephalographic signals. Preliminary results on the application of BCI with real experimental subjects allow to envision a rehabilitation scenario using this kind of technology as part of the therapeutic protocols in a near future. Yet, some problems need to be solved: improve target detection performance and the generation of natural commands by non-invasive brain-computer interfaces based on surface electroencephalography are some of them. In this work, we make a review of the most important researches to exhibit the evolution, the current status, and the research opportunities on the use of brain-computer interfaces for upper limb neurorehabilitation in spinal cord injury patients.

Resumo O número de trabalhos relacionados com Interfaces Cérebro-Computador (BCI, Brain-Computer Interface en inglés) diretamente aplicado no processo de reabilitação de pacientes com lesões de medula espinal está no aumento e a melhoria nas técnicas de processamento digital de sinais e reconhecimento de patrones que permitam relacionar as seqüências de eletroencefalográficas com ações motoras. Os resultados preliminares das provas de BCI sobre sujeitos reais permitem visualizar em um futuro, mais perto da inclusão deste tipo de ferramentas em protocolos de reabilitação. Sin embargo, feno muitas barreras por resolver, principalmente as relacionadas com o aumento do desempenho e a geração de comandos comandos por meio de interfaces cerebro-computador a partir de electroencefalografía superficial (EEG). No presente trabalho, a empresa tem uma revisão dos mais importantes trabalhos e mostra as evoluções, o estado real e as oportunidades de pesquisa em torno da temática de interfaces cerebro-computador em processos de neurorrehabilitação de supostos superiores en pacientes com lesiones medulares.

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Background: For some time now, the effects of sound, noise, and music on the human body have been studied. However, despite research done through time, it is still not completely clear what influence, interaction, and effects sounds have on human body. That is why it is necessary to conduct new research on this topic. Thus, in this paper, a systematic review is undertaken in order to integrate research related to several types of sound, both pleasant and unpleasant, specifically noise and music. In addition, it includes as much research as possible to give stakeholders a more general vision about relevant elements regarding methodologies, study subjects, stimulus, analysis, and experimental designs in general. This study has been conducted in order to make a genuine contribution to this area and to perhaps to raise the quality of future research about sound and its effects over ECG signals. Methods: This review was carried out by independent researchers, through three search equations, in four different databases, including: engineering, medicine, and psychology. Inclusion and exclusion criteria were applied and studies published between 1999 and 2017 were considered. The selected documents were read and analyzed independently by each group of researchers and subsequently conclusions were established between all of them. Results: Despite the differences between the outcomes of selected studies, some common factors were found among them. Thus, in noise studies where both BP and HR increased or tended to increase, it was noted that HRV (HF and LF/HF) changes with both sound and noise stimuli, whereas GSR changes with sound and musical stimuli. Furthermore, LF also showed changes with exposure to noise. Conclusion: In many cases, samples displayed a limitation in experimental design, and in diverse studies, there was a lack of a control group. There was a lot of variability in the presented stimuli providing a wide overview of the effects they could produce in humans. In the listening sessions, there were numerous examples of good practice in experimental design, such as the use of headphones and comfortable positions for study subjects, while the listening sessions lasted 20 min in most of the studies.

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Una etapa importante y fundamental en el reconocimiento de patrones sobre imágenes es la determinación del conjunto de características que mejor pueda describir la misma. En este artículo se presenta una etapa adicional entre la caracterización de la imagen y su posterior clasificación o recuperación de imágenes similares a una imagen dada, conocido como análisis de relevancia. Este permite reducir la dimensionalidad del conjunto inicial de características a un nuevo conjunto de menor dimensión que conserva la tasa de acierto de la recuperación. Las imágenes analizadas correspondieron a nódulos pulmonares de placas radiológicas de tórax disponibles en una base de datos de acceso libre disponible a través de la sociedad japonesa de tecnología radiológica. Se analizaron algoritmos de selección de características basados en filtros que incluyeron los métodos FOCUS, RELIEEF-F y Branch & Bound (B&B). Estos algoritmos fueron modificados e implementados en C++. En el caso de RELIEF-F se logró obtener un ahorro del 34% de características sin afectar la tasa de recuperación cuando se empleaba el 100% de las características originales. Asimismo, el algoritmo implementado presentó un desempeño superior al algoritmo original disponible en la herramienta de código abierto Weka. Asimismo se implementó una estrategia de ponderación de pesos aplicada a las características identificadas cuando se utilizaron los algoritmos RELIEF-F, FOCUS y B&B simultáneamente. Dicha estrategia permitió ponderar cada característica de acuerdo a su participación en los conjuntos mínimos de características relevantes y determinar la consistencia de los mismos. La estrategia de pesos permitió un ahorro del 48% de características necesarias para la recuperación, aunque la tasa de recuperación fue disminuida de 77% a 76%.

An important and fundamental stage in the image pattern recognition is the determination of the characteristics set that best describes the image. This paper describes a further step between the image characterization and its posterior classification or image retrieval similar to a given image, known as relevance analysis. It allows reducing the dimensionality of an initial set of features to a new set with fewer dimensions that preserves the hit rate of the retrieval. The analyzed images corresponded to lung nodules of radiological plaques of thorax, available through the open access library available through the Japanese society of radiological technology. To achieve these results, characteristic selection algorithms based on different filters such as FOCUS, RELIEEF-F, and BRANCH & BOUND (B&B) were analyzed. In the case of RELIEF-F it was possible to save as much as 34% of the initial characteristics set without affecting the retrieval rate compared to when the 100% of characteristics were used. Further, the implemented algorithm achieved a superior performance to that of the original algorithm included in the validated Weka software. Likewise, a strategy consisting in weights averaging was implemented that was applied to identified characteristics when the algorithms RELIEF-F, FOCUS and B&B were used simultaneously. Such weighting scheme, allowed the averaging of each characteristic according to its contribution in the minimal set of relevant features, allowing to determinate their consistency. The weighting strategy allowed a 48% reduction in the characteristics, although the retrieval hit rate slightly decreased from 77% to 76%.