Pattern recognition at different scales: a statistical perspective

In this paper we borrow concepts from Information Theory and Statistical Mechanics to perform a pattern recognition procedure on a set of x-ray hazelnut images. We identify two relevant statistical scales, whose ratio affects the performance of a machine learning algorithm based on statistical observables, and discuss the dependence of such scales on the image resolution. Finally, by averaging the performance of a Support Vector Machines algorithm over a set of training samples, we numerically verify the predicted onset of an optimal scale of resolution, at which the pattern recognition is favoured

Statistical Techniques and Artificial Neural Networks for Image Analysis

The main topic of this PhD thesis is image analysis. The subject was investigated from different perspectives, starting from different image types and with different goals. At the beginning x-ray hazelnuts images were analyzed. The target of this analysis was to determine whether a hazelnut was good or not. In order to do this an Artificial Neural Network was used, whose features were statistical variables. At a later stage a SVM was implemented to try to get better results. The second kind of images were still x-ray ones; they were, however, images coming from a PCB productive process. What we were asked to do was to highlight the air bubbles trapped into a solder joint (particularly those inside the thermal pads). In this case filters and morphological operations were used. The third case were ulcers photographs: the goal of the collaboration with SIF (Società Italiana di Flebologia, Italian society of phlebology) is to give doctors a way to evaluate ulcers remotely, in order to customize the treatments according to how the healing behaves. A small digression was the development of a small and cheap Arduino-based robot for an educational laboratory (Xkè?, in collaboration with prof. Angelo Raffaele Meo from DAUIN, Politecnico di Torino). This should have been the first step towards the development of an evolved robot for agricultural purposes, but the project didn’t start

Smartphone and Bluetooth Smart Sensor Usage in IoT Applications

Bluetooth Low Energy is an interesting short-range radio technology that could be used for connecting tiny devices into the Internet of Things (IoT) through gateways or cellular networks. For example, they are widely used in various contexts, from building and home automation to wearables. This paper proposes a method to improve the use of smartphones with a smart wireless sensor network acquisition system through Bluetooth Low Energy (BLE). A new BLE Smart Sensor, which acquires environmental data, was designed and calibration methods were performed. A detailed deviation is calculated between reference sensor and sensor node. The data obtained from laboratory experiments were used to evaluate battery life of the node. An Android application for devices such as Smartphones and Tablets can be used to collect data from a smart sensor, which becomes more accurate

The Use of Bluetooth Low Energy Smart Sensor for Mobile Devices Yields an Efficient Level of Power Consumption

Mobile devices, such as smartphones and tablets have already become an integral part of our lives. For example, they are widely used throughout society with several applications in various business sectors which include smart houses, irrigation systems, healthcare and many more. This paper proposes a method to improve the use of smartphones with a smart wireless sensor network acquisition system through Bluetooth Low Energy (BLE). A new BLE Smart Sensor, which acquires environmental data was designed. This can be used with normal android devices (Smartphones, Tablets) to collect information from a smart sensor. Moreover, a BLE acquisition algorithm was successfully implemented on the firmware of the device

Low Power and Bluetooth-Based Wireless Sensor Network for Environmental Sensing Using Smartphones

Part 6: Third Intelligent Systems for Quality of Life Information Services Workshop (ISQL 2012)International audienceCurrent research and improvements in the field of wireless sensor networks are focused on decreasing the power consumption and miniaturization, improved smartness and better wearability of the sensor, and especially with their capability for environmental sensing. Today, the survival of these kinds of networks is a critical issue especially in order to keep environmental information updated. This paper presents, an improvement of the environmental sensing acquisition system shown in [1], by applying more sensors to gather data. It was found a novel method of reading sensor data using smartphones and also the structure of sensors themselves helps to decrease the power consumption of the network

Right ventricular global work efficiency:A reliable non-invasive estimate of right ventricular contractility

BACKGROUND: Right ventricular (RV) myocardial work (RVMW) recently emerged as a non-invasive alternative for the assessment of RV contractility. However, none of the prior studies assessed its variations under different haemodynamic conditions. We aimed to evaluate the variations of the components of RVMW in heart failure (HF) patients with pulmonary hypertension (PH) undergoing a reversibility test. METHODS: Consecutive HF patients with reduced ejection fraction who underwent right heart catheterization and echocardiography at our institution were prospectively enrolled. Patients with PH and augmented pulmonary vascular resistance who achieved normalization of pulmonary pressures after the reversibility test using vasodilators underwent a second echocardiographic assessment under the same haemodynamic conditions. Four components of RVMW were analysed: (1) RV global work index (mmHg%); (2) RV global constructive work (mmHg%); (3) RV global wasted work (RVGWW) (mmHg%); (4) RV global work efficiency (RVGWE) (%). RESULTS: One hundred two patients were enrolled (53 with PH and 49 without). Global RVMW was higher in patients with PH, due to a significantly higher RVGWW [81 (55-119) mmHg% vs. 49 (28-72) mmHg%; P = 0.013], while RVGWE was similar between the two groups (80 ± 10% vs. 82 ± 12%; P = 0.332). In patients with PH, 27/52 (51.9%) had combined PH, while 25/52 (48.1%) had isolated post-capillary PH. A reversibility test was performed in 26/27 (96.2%) patients with combined PH and pulmonary pressure normalization was observed in 16/26 (61.5%) subjects. In patients with PH normalization, RVGWE remained almost unchanged (from 82.8 ± 6.9% to 85.3 ± 6.6%; P = 0.596), while RVGWW significantly decreased [from 60 (49-90) mmHg% to 41 (31-53) mmHg%; P = 0.027]. Among all the echocardiographic and haemodynamic parameters adopted for assessing RV function, RVGWE was the least variable during the reversibility test (mean variation 3 ± 10%). CONCLUSIONS: RVGWE is comparable between HF patients with and without PH and remains stable across different haemodynamic conditions. This consistency suggests that it can be a reliable parameter for assessing RV contractility. Larger studies are needed to confirm this hypothesis and to test its prognostic significance