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Light emitting diodes (LED) are becoming the dominant lighting elements due to their efficiency. Optical camera communications (OCC), the branch of visible light communications (VLC) that uses video cameras as receivers, is a suitable candidate in facilitating the development of new communication solutions for the broader public because video cameras are available on almost any smartphone nowadays. Unfortunately, most OCC systems that have been proposed until now require either expensive and specialized high-frame-rate cameras as receivers, which are unavailable on smartphones, or they rely on the rolling shutter effect, being sensitive to camera movement and pointing direction, they produce light flicker when low-frame-rate cameras are used, or they must discern between more than two light intensity values, affecting the robustness of the decoding process. This paper presents in detail the design of an OCC system that overcomes these limitations, being designed for receivers capturing 120 frames per second and being easily adaptable for any other frame rate. The system does not rely on the rolling shutter effect, thus making it insensitive to camera movement during frame acquisition and less demanding about camera resolution. It can work with reflected light, requiring neither a direct line of sight to the light source nor high resolution image sensors. The proposed communication is invariant to the moment when the transmitter and the receiver are started as the communication is self-synchronized, without any other exchange of information between the transmitter and the receiver, without producing light flicker, and requires only two levels of brightness to be detected (light on and light off). The proposed system overcomes the challenge of not producing light flicker even when it is adapted to work with very low-frame-rate receivers. This paper presents the statistical analysis of the communication performance and discusses its implementation in an indoor localization system.

Hepatocellular Carcinoma (HCC) is the most common malignant liver tumor, being present in 70% of liver cancer cases. It usually evolves on the top of the cirrhotic parenchyma. The most reliable method for HCC diagnosis is the needle biopsy, which is an invasive, dangerous method. In our research, specific techniques for non-invasive, computerized HCC diagnosis are developed, by exploiting the information from ultrasound images. In this work, the possibility of performing the automatic diagnosis of HCC within B-mode ultrasound and Contrast-Enhanced Ultrasound (CEUS) images, using advanced machine learning methods based on Convolutional Neural Networks (CNN), was assessed. The recognition performance was evaluated separately on B-mode ultrasound images and on CEUS images, respectively, as well as on combined B-mode ultrasound and CEUS images. For this purpose, we considered the possibility of combining the input images directly, performing feature level fusion, then providing the resulted data at the entrances of representative CNN classifiers. In addition, several multimodal combined classifiers were experimented, resulted by the fusion, at classifier, respectively, at the decision levels of two different branches based on the same CNN architecture, as well as on different CNN architectures. Various combination methods, and also the dimensionality reduction method of Kernel Principal Component Analysis (KPCA), were involved in this process. These results were compared with those obtained on the same dataset, when employing advanced texture analysis techniques in conjunction with conventional classification methods and also with equivalent state-of-the-art approaches. An accuracy above 97% was achieved when our new methodology was applied.

Background Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. Methods Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. Results Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy. Conclusion miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

Hepatocellular Carcinoma (HCC) is the most frequent malignant liver tumor and the third cause of cancer-related deaths worldwide. For many years, the golden standard for HCC diagnosis has been the needle biopsy, which is invasive and carries risks. Computerized methods are due to achieve a noninvasive, accurate HCC detection process based on medical images. We developed image analysis and recognition methods to perform automatic and computer-aided diagnosis of HCC. Conventional approaches that combined advanced texture analysis, mainly based on Generalized Co-occurrence Matrices (GCM) with traditional classifiers, as well as deep learning approaches based on Convolutional Neural Networks (CNN) and Stacked Denoising Autoencoders (SAE), were involved in our research. The best accuracy of 91% was achieved for B-mode ultrasound images through CNN by our research group. In this work, we combined the classical approaches with CNN techniques, within B-mode ultrasound images. The combination was performed at the classifier level. The CNN features obtained at the output of various convolution layers were combined with powerful textural features, then supervised classifiers were employed. The experiments were conducted on two datasets, acquired with different ultrasound machines. The best performance, above 98%, overpassed our previous results, as well as representative state-of-the-art results.

The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and computer-aided diagnosis within ultrasound images, where the amount of available annotated data is smaller, a natural question arises: are deep-learning methods better than conventional machine-learning methods? How do the conventional machine-learning methods behave in comparison with deep-learning methods on the same dataset? Based on the study of various deep-learning architectures, a lightweight multi-resolution Convolutional Neural Network (CNN) architecture is proposed. It is suitable for differentiating, within ultrasound images, between the Hepatocellular Carcinoma (HCC), respectively the cirrhotic parenchyma (PAR) on which HCC had evolved. The proposed deep-learning model is compared with other CNN architectures that have been adapted by transfer learning for the ultrasound binary classification task, but also with conventional machine-learning (ML) solutions trained on textural features. The achieved results show that the deep-learning approach overcomes classical machine-learning solutions, by providing a higher classification performance.

Renal cancer (RC) represents 3% of all cancers, with a 2% annual increase in incidence worldwide, opening the discussion about the need for screening. However, no established screening tool currently exists for RC. To tackle this issue, we assessed surface-enhanced Raman scattering (SERS) profiling of serum as a liquid biopsy strategy to detect renal cell carcinoma (RCC), the most prevalent histologic subtype of RC. Thus, serum samples were collected from 23 patients with RCC and 27 controls (CTRL) presenting with a benign urological pathology such as lithiasis or benign prostatic hypertrophy. SERS profiling of deproteinized serum yielded SERS band spectra attributed mainly to purine metabolites, which exhibited higher intensities in the RCC group, and Raman bands of carotenoids, which exhibited lower intensities in the RCC group. Principal component analysis (PCA) of the SERS spectra showed a tendency for the unsupervised clustering of the two groups. Next, three machine learning algorithms (random forest, kNN, naïve Bayes) were implemented as supervised classification algorithms for achieving discrimination between the RCC and CTRL groups, yielding an AUC of 0.78 for random forest, 0.78 for kNN, and 0.76 for naïve Bayes (average AUC 0.77 ± 0.01). The present study highlights the potential of SERS liquid biopsy as a diagnostic and screening strategy for RCC. Further studies involving large cohorts and other urologic malignancies as controls are needed to validate the proposed SERS approach.

Ultrasound is becoming a fundamental first-line diagnostic tool for most medical specialties and an innovative tool to teach anatomy, physiology and pathophysiology to undergraduate and graduate students. However, availability of structured training programs during medical school is lagging behind and many physicians still acquire all their ultrasound skills during postgraduate training.There is wide variation in medical student ultrasound education worldwide. Sharing successful educational strategies from early adopter medical schools and learning from leading education programs should advance the integration of ultrasound into the university medical school curricula. In this overview, we present current approaches and suggestions by ultrasound societies concerning medical student educa-tion throughout the world. Based on these examples, we formulate a consensus statement with suggestions on how to integrate ultrasound teaching into the preclinical and clinical medical curricula.

Robust detection of emergency vehicle sirens remains difficult due to modern soundproofing, competing audio, and variable traffic noise. Although many simulation-based studies have been reported, relatively few systems have been realized in hardware, and many proposed approaches rely on complex or artificial intelligence-based processing with limited interpretability. This work presents a physical implementation of a low-complexity yelp siren detector that leverages the symmetries of the yelp signal, together with its characterization under realistic conditions. The design is not based on conventional signal processing or machine learning pipelines. Instead, it uses a simple analog envelope-based principle with threshold-crossing rate analysis and a fixed comparator threshold. Its performance was evaluated using an open dataset of more than 1000 real-world audio recordings spanning different road conditions. Detection accuracy, false-positive behavior, and robustness were systematically evaluated on a real hardware implementation using multiple deployable decision rules. Among the evaluated detection rules, a representative operating point achieved a true positive rate of 0.881 at a false positive rate of 0.01, corresponding to a Matthews correlation coefficient of 0.899. The results indicate that a fixed-threshold realization can provide reliable yelp detection with very low computational requirements while preserving transparency and ease of implementation. The study establishes a pathway from conceptual detection principle to deployable embedded hardware.

High-frequency ultrasound is a noninvasive tool that offers characteristic markers, quantifying the cutaneous changes of the physiological senescence process. The aim was to assess the changes in skin thickness, dermal density and echogenicity, as part of the ageing process, with different age intervals. The study was performed on 160 patients, aged 40.4 ± 21.2, divided into four age categories: <20, 21-40, 41-60, 61-80. Ultrasonographic images (Dermascan device) were taken from three sites: dorsal forearm (DF), medial arm (MA), zygomatic area (ZA). We assessed the thickness of epidermis and dermis (mm), number of low, medium, high echogenicity pixels, the ratio between the echogenicity of the upper and lower dermis (LEPs/LEPi), and SLEB (subepidermal low echogenicity band). The statistical analysis was performed using SPSS 15.00. A P value <0.05 was considered significant. On all examined sites, it was found that the dermal thickness increases in the 21 to 40 year interval (P<0.0001). After the 21 to 40 year interval, the number of low echogenic pixels increases significantly, especially on photoexposed sites. High-echogenic pixels follow the same pattern on all examined sites: they increase in the 21 to 40 year interval and decrease in the 3rd and 4th age category. The LEPs/LEPi ratio increases significantly with age, at all sites (P<0.05), due to an increase of hypoechogenic pixels in the upper dermis. High-frequency ultrasound is a noninvasive \"histological\" tool that can assess the cutaneous structure and age-related changes. It offers imagistic markers, comparable to the histological parameters and also characteristic ultrasonographic markers. Histology remains the gold standard for the investigation of the integumentary system.

Aim: The purpose of this study is to evaluate pancreatic stiffness by ARFI abdominal elastography. In the current literature, there are relatively few studies that have assessed the clinical utility of this technique. Material and method: A number of 37 healthy subjects were included. The data were collected in a prospective manner and afterwards included in an observational, analytical and longitudinal study. Subsequently viewing the pancreatic parenchyma in bidimensional mode (2D-US) mode, 10 shear wave velocity (SWV) measurements for each segment: head, body and tail were performed. Statistical analysis by regression models targeted also the possible influence of other factors in assessing SWV. A comparative analysis was performed regarding the statistical significance of 5 versus 10 SWV measurements for each segment. Results: The pancreas was entirely evaluated in all subjects. The mean SWV from the entire parenchyma was 1.216 m/s±0.36 and between the three segments SWV were similar (head: 1.224 m/s, body: 1.227 m/s and tail: 1.191 m/s). A ratio of the IQR/Median >0.4 was interpreted as statistically invalid, relevant data being highlighted in the percentage of 83.78% for the head of the pancreas, 78.37% for the body, and 67.56% at the caudal level. Significant correlations were observed between the data (mean and median SWV) provided by the group with 5 measurements of the SWV versus the standard group: 93.9% for the head, 96.6% for the body, and 98.7% accordingly to the tail. Conclusions: SWV determination by percutaneous approach represents a useful imaging method for evaluating pancreatic stiffness, of course within these limitations. Because we did not observe statistically significant differences between the results obtained by 5 or 10 measurements, we suggest that it would be sufficient to perform only five measurements of the SWV per pancreatic segment. The data obtained in the normal pancreas could be used in future comparative assessments regarding the inflammatory or tumoral pathology of the pancreas.