Analysis and application of digital spectral warping in analog and mixed-signal testing

Spectral warping is a digital signal processing transform which shifts the frequencies contained within a signal along the frequency axis. The Fourier transform coefficients of a warped signal correspond to frequency-domain 'samples' of the original signal which are unevenly spaced along the frequency axis. This property allows the technique to be efficiently used for DSP-based analog and mixed-signal testing. The analysis and application of spectral warping for test signal generation, response analysis, filter design, frequency response evaluation, etc. are discussed in this paper along with examples of the software and hardware implementation

Automated border control systems: biometric challenges and research trends

Automated Border Control (ABC) systems automatically verify the travelers\u2019 identity using their biometric information, without the need of a manual check, by comparing the data stored in the electronic document (e.g., the e-Passport) with a live sample captured during the crossing of the border. In this paper, the hardware and software components of the biometric systems used in ABC systems are described, along with the latest challenges and research trends

A decision support system for wind power production

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

Adaptive ECG biometric recognition : a study on re-enrollment methods for QRS signals

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

Methylglyoxal, Glycated Albumin, PAF, and TNF-α: Possible Inflammatory and Metabolic Biomarkers for Management of Gestational Diabetes

Background: In gestational diabetes mellitus (GDM), pancreatic \u3b2-cell breakdown can result from a proinflammatory imbalance created by a sustained level of cytokines. In this study, we investigated the role of specific cytokines, such as B-cell activating factor (BAFF), tumor necrosis factor \u3b1 (TNF-\u3b1), and platelet-activating factor (PAF), together with methylglyoxal (MGO) and glycated albumin (GA) in pregnant women affected by GDM. Methods: We enrolled 30 women whose inflammation and metabolic markers were measured at recruitment and after 12 weeks of strict dietetic therapy. We compared these data to the data obtained from 53 randomly selected healthy nonpregnant subjects without diabetes, hyperglycemia, or any condition that can affect glycemic metabolism. Results: In pregnant women affected by GDM, PAF levels increased from 26.3 (17.4-47.5) ng/mL to 40.1 (30.5-80.5) ng/mL (p < 0.001). Their TNF-\u3b1 levels increased from 3.0 (2.8-3.5) pg/mL to 3.4 (3.1-5.8) pg/mL (p < 0.001). The levels of methylglyoxal were significantly higher in the women with GDM (p < 0.001), both at diagnosis and after 12 weeks (0.64 (0.46-0.90) \u3bcg/mL; 0.71 (0.47-0.93) \u3bcg/mL, respectively) compared to general population (0.25 (0.19-0.28) \u3bcg/mL). Levels of glycated albumin were significantly higher in women with GDM (p < 0.001) only after 12 weeks from diagnosis (1.51 (0.88-2.03) nmol/mL) compared to general population (0.95 (0.63-1.4) nmol/mL). Conclusion: These findings support the involvement of new inflammatory and metabolic biomarkers in the mechanisms related to GDM complications and prompt deeper exploration into the vicious cycle connecting inflammation, oxidative stress, and metabolic results

Weight Loss Improves Cardio-Metabolic and Inflammatory State in Subjects with Metabolic Syndrome

Metabolic syndrome (MetS) is a condition characterized by a constellation of reversible major risk factors for cardiovascular disease (CVD) and type 2 diabetes (T2DM). While it has been widely demonstrated that weight reduction by 5\u201310% decreases CVD and T2DM risk factors, including atherogenic dyslipidemia, on the other hand, its effects on comprehensive serum cytokine profile and endotoxemia are less investigated. Furthermore, the impact of weight loss on these parameters was studied especially in subjects with morbid obesity, often after bariatric surgery; while the studies on the effects of a physiological weight reduction with a balanced hypocaloric diet in overweight and moderately obese subjects showed contradictory results

Supporting Application Requirements in Cloud-based IoT Information Processing

IoT infrastructures can be seen as an interconnected network of sources of data, whose analysis and processing can be beneficial for our society. Since IoT devices are limited in storage and computation capabilities, relying on external cloud providers has recently been identified as a promising solution for storing and managing IoT data. Due to the heterogeneity of IoT data and applicative scenarios, the cloud service delivery should be driven by the requirements of the specific IoT applications. In this paper, we propose a novel approach for supporting application requirements (typically related to security, due to the inevitable concerns arising whenever data are stored and managed at external third parties) in cloud-based IoT data processing. Our solution allows a subject with an authority over an IoT infrastructure to formulate conditions that the provider must satisfy in service provisioning, and computes a SLA based on these conditions while accounting for possible dependencies among them. We also illustrate a CSP-based formulation of the problem of computing a SLA, which can be solved adopting off-the-shelves CSP solvers

Supporting User Requirements and Preferences in Cloud Plan Selection

With the cloud emerging as a successful paradigm for conveniently storing, accessing, processing, and sharing information, the cloud market has seen an incredible growth. An ever-increasing number of providers offer today several cloud plans, with different guarantees in terms of service properties such as performance, cost, or security. While such a variety naturally corresponds to a diversified user demand, it is far from trivial for users to identify the cloud providers and plans that better suit their specific needs. In this paper, we address the problem of supporting users in cloud plan selection. We characterize different kinds of requirements that may need to be supported in cloud plan selection and introduce a very simple and intuitive, yet expressive, language that captures different requirements as well as preferences users may wish to express. The corresponding formal modeling permits to reason on requirements satisfaction to identify plans that meet the constraints imposed by requirements, and to produce a preference-based ranking among such plans

A Fuzzy-Based Brokering Service for Cloud Plan Selection

The current cloud market features a multitude of cloud services that differ from one another in terms of functionality or of security/performance guarantees. Users wishing to use a cloud service for storing, processing, or sharing their data must be able to select the service that best matches their desiderata. In this paper, we propose a novel, user centric, brokering service for supporting users in the specification of requirements and enabling their evaluation against available cloud plans, assessing how much the different plans can satisfy the user\u2019s desiderata. Our brokering service allows users to specify their desiderata in an easy and intuitive way by using natural language expressions and high-level concepts. Fuzzy logic and fuzzy inference systems are adopted to quantitatively assess the compliance of cloud services with the users\u2019 desiderata, and hence to help users in the cloud service selection process

Analyzing images in frequency domain to estimate the quality of wood particles in OSB production

The analysis of the quality of particulate materials is of great importance for a variety of research and industrial applications. Most image-based methods rely on the segmentation of the image to measure the particles and aggregate their characteristics. However, the segmentation of particulate materials can be severely affected when the setup is not controlled. For instance, when there are device errors, changes in the light conditions, or when the camera gets dirty because of the dust or a similar substance. All of these circumstances are common in industrial setups, like the one studied in this paper. This work presents a framework for quality estimation based on image processing algorithms that avoids segmentation. The considered application scenario is the online quality control of the production of Oriented Strand Boards (OSB), a type of wood panel frequently used in construction and manufacturing industries. The proposed method quantizes frequency domain into a histogram using a non-parametric method, which is later exploited using computational intelligence to classify the quality of superimposed wood particles deposed on a conveyor belt. The method has been tested using synthetic and real images with different noise conditions. The results illustrate the robustness of the approach and its capability to detect significant quality changes in the wood particles