The incidence of melanoma in situ (MIS) is growing significantly. Detection at the MIS stage provides the highest cure rate for melanoma, but reliable detection of MIS with dermoscopy alone is not yet possible. Adjunct dermoscopic instrumentation using digital image analysis may allow more accurate detection of MIS. Gray areas are a critical component of MIS diagnosis, but automatic detection of these areas remains difficult because similar gray areas are also found in benign lesions. This paper proposes a novel adaptive thresholding technique for automatically detecting gray areas specific to MIS. The proposed model uses only MIS dermoscopic images to precisely determine gray area characteristics specific to MIS. To this aim, statistical histogram analysis is employed in multiple color spaces. It is demonstrated that skew deviation due to an asymmetric histogram distorts the color detection process. We introduce a skew estimation technique that enables histogram asymmetry correction facilitating improved adaptive thresholding results. These histogram statistical methods may be extended to detect any local image area defined by histograms.

The purpose of this paper is to develop a diagnostic tool that can analyze light microscope images of human oocytes and derive a description of the oocyte cytoplasm that is useful for quality assessment in assisted insemination. The proposed approach includes three main phases: 1) segmentation; 2) feature extraction; and 3) clustering. In the segmentation phase, a region of interest inside the cytoplasm is extracted through morphological operators and the Hough transform. In the second phase, regions that result from segmentation are processed through a multiresolution texture analysis to extract a set of features that describe different levels of cytoplasm granularity. To this aim, we evaluate some statistics in the Haar wavelet transform domain. Finally, the extracted features are used to cluster oocytes according to different levels of granularity. This approach is made by fuzzy clustering. Experimental results on a collection of microscope images of oocytes are reported to show the effectiveness of the proposed approach. In addition, comparison with alternative methods for feature extraction and clustering is performed.

Renewable energy production is constantly growing worldwide, and some countries produce a relevant percentage of their daily electricity consumption through wind energy. Therefore, decision support systems that can make accurate predictions of wind-based power production are of paramount importance for the traders operating in the energy market and for the managers in charge of planning the nonrenewable energy production. In this paper, we present a decision support system that can predict electric power production, estimate a variability index for the prediction, and analyze the wind farm (WF) production characteristics. The main contribution of this paper is a novel system for long-term electric power prediction based solely on the weather forecasts; thus, it is suitable for the WFs that cannot collect or manage the real-time data acquired by the sensors. Our system is based on neural networks and on novel techniques for calibrating and thresholding the weather forecasts based on the distinctive characteristics of the WF orography. We tuned and evaluated the proposed system using the data collected from two WFs over a two-year period and achieved satisfactory results. We studied different feature sets, training strategies, and system configurations before implementing this system for a player in the energy market. This company evaluated the power production prediction performance and the impact of our system at ten different WFs under real-world conditions and achieved a significant improvement with respect to their previous approach.

Electrical load forecasting has a fundamental role in the decision-making process of energy system operators. When many users are connected to the grid, high-performance forecasting models are required, posing several problems associated with the availability of historical energy consumption data for each end-user and training, deploying and maintaining a model for each user. Moreover, introducing new end-users to an existing network poses problems relating to their forecasting model. Global models, trained on all available data, are emerging as the best solution in several contexts, because they show higher generalization performance, being able to leverage the patterns that are similar across different time series. In this work, the lodging/residential electricity 1-h-ahead load forecasting of multiple time series for smart grid applications is addressed using global models, suggesting the effectiveness of such an approach also in the energy context. Results obtained on a subset of the Great Energy Predictor III dataset with several global models are compared to results obtained with local models based on the same methods, showing that global models can perform similarly to the local ones, while presenting simpler deployment and maintainability. In this work, the forecasting of a new time series, representing a new end-user introduced in the pre-existing network, is also approached under specific assumptions, by using a global model trained using data related to the existing end-users. Results reveal that the forecasting model pre-trained on data related to other end-users allows the attainment of good forecasting performance also for new end-users.

The diffusion of wearable and mobile devices for the acquisition and analysis of cardiac signals drastically increased the possible applicative scenarios of biometric systems based on electrocardiography (ECG). Moreover, such devices allow for comfortable and unconstrained acquisitions of ECG signals for relevant time spans of tens of hours, thus making these physiological signals particularly attractive biometric traits for continuous authentication applications. In this context, recent studies showed that the QRS complex is the most stable component of the ECG signal, but the accuracy of the authentication degrades over time, due to significant variations in the patterns for each individual. Adaptive techniques for automatic template update can therefore become enabling technologies for continuous authentication systems based on ECG characteristics.

Last few years have witnessed an ever-increasing demand of border crossing, whose processing introduces the need to speed-up the clearance process at the Border Crossing Points (BCP). Automated Border Control (ABC) gates, or shortly e-Gates, can verify the identity of the travelers crossing the borders by exploiting their biometric traits, without the need of a constant human intervention. Biometric technologies have a relevant impact on the improvement of efficiency, effectiveness and security of the checking processes. Automated biometric recognition can increase the border processing throughput of the BCP, as well as facilitate the clearance procedures. To grant the passage of the border, the e-Gate compares the biometric samples of the traveler stored into the electronic document with live acquisitions. This paper presents the latest substantial advances in the design of e-Gates. In particular, it presents the Biometric Verification System in detail, including its hardware and software components, as well as the procedures followed during the biometric verification of the traveler's identity. We address the complex issue of measuring the performance of an ABC system, considering the real applicability of the figures of merit usually adopted in biometric system's evaluation. To complete the view of the current e-Gates, we highlight the main challenges and the research trends relating to the biometric systems currently used in e-Gates.