Waterloss Detection in Water Distribution Networks using Wavelet Change-Point Detection

The work presented herein addresses the automatic detection of water losses in water distribution networks (WDN), through the dynamic analysis of the time series related to water consumption within the network and the use of a wavelet change-point detection classifier for identifying anomalies in the consumption patterns. The wavelet change-point method utilizes the continuous wavelet transform (CWT) of time-series (signals) to analyze how the frequency content of a signal changes over time. In the case of water distribution networks the time-series relates to streaming water consumption data from automatic meter reading (AMR) devices, at either the individual consumers’ level or at an aggregated district meter area (DMA) level. The wavelet change-point detection method analyzes the provided time-series to acquire inherent knowledge on water consumption under normal conditions at household or area-wide levels, to then make inferences about water consumption under abnormal conditions. The method is demonstrated on several abnormal WDN operating conditions and anomaly detection cases.     

Geometric Methods for Invariant‐Zero Cancellation in Linear Multivariable Systems with Application to Signal Rejection with Preview

This work deals with zero cancellation in linear multivariable systems with possible feedthrough terms from the inputs to the outputs. A methodology for the synthesis of a minimal‐order feedforward compensator preserving key properties of the original system while cancelling minimum‐phase invariant zeros is derived by means of the basic tools of the geometric approach. The properties maintained in the feedforward compensation scheme are stabilizability and right‐invertibility. Duality arguments show how to modify the devised method so as to achieve zero cancellation with a cascade filter retaining detectability and left‐invertibility. Continuous and discrete‐time systems are considered in a unified framework exploiting the common structural features. An original application of zero cancellation to signal rejection with preview is presented. A novel feedforward control scheme is devised, avoiding the steering along minimum‐phase zero techniques that are at the basis of well‐settled solutions.     

Relation between PVT measurements and linear viscosity in isotactic and syndiotactic polypropylenes

Pressure-Volume-Temperature (PVT) data of an isotactic and a syndiotactic PP are fitted to a modification of the Simha-Somcynsky equation of state (S-S), to calculate characteristic parameters, such as V^∗, T^∗, P^∗ and the solubility parameter δ. The hole fraction of the S-S model and the free volume are deduced from these parameters. The application of a modified Doolittle equation and a modified Berry-Fox equation to estimate viscosity, leads to extract novel conclusions on the differences between both types of PPs. An equation which accounts for the effect of temperature on the characteristic ratio of syndiotactic PP is presented.     

A first assessment of the SMOS data in southwestern France using in situ and airborne soil moisture estimates: The CAROLS airborne campaign

The Soil Moisture and Ocean Salinity (SMOS) satellite mission, based on an aperture synthesis L-band radiometer was successfully launched in November 2009. In the context of a validation campaign for the SMOS mission, intensive airborne and in situ observations were performed in southwestern France for the SMOS CAL/VAL, from April to May 2009 and from April to July 2010. The CAROLS (Cooperative Airborne Radiometer for Ocean and Land Studies) bi-angular (34°-0°) and dual-polarized (V and H) L-band radiometer was designed, built and installed on board the French ATR-42 research aircraft. During springs of 2009 and 2010, soil moisture observations from the SMOSMANIA (Soil Moisture Observing System-Meteorological Automatic Network Integrated Application) network of Météo-France were complemented by airborne observations of the CAROLS L-band radiometer, following an Atlantic-Mediterranean transect in southwestern France. Additionally to the 12 stations of the SMOSMANIA soil moisture network, in situ measurements were collected in three specific sites within an area representative of a SMOS pixel. Microwave radiometer observations, acquired over southwestern France by the CAROLS instrument were analyzed in order to assess their sensitivity to surface soil moisture (w_g). A combination of microwave brightness temperature (T_b) at either two polarizations or two contrasting incidence angles was used to retrieve w_g through regressed empirical logarithmic equations with good results, depending on the chosen configuration. The regressions derived from the CAROLS measurements were applied to the SMOS T_b and their retrieval performance was evaluated. The retrievals of w_g showed significant correlation (p-value < 0.05) with surface measurements for most of the SMOSMANIA stations (8 of 12 stations) and with additional field measurements at two specific sites, also. Root mean square errors varied from 0.03 to 0.09 m³ m^− 3 (0.06 m³ m^− 3 on average).     

Strong normalization from an unusual point of view

A new complete characterization of β-strong normalization is given, both in the classical and in the lazy λ-calculus, through the notion of potential valuability inside two suitable parametric calculi.     

Energy-saving models for wireless sensor networks

Nowadays, wireless sensor networks are being used for a fast-growing number of different application fields (e.g., habitat monitoring, highway traffic monitoring, remote surveillance). Monitoring (i.e., querying) the sensor network entails the frequent acquisition of measurements from all sensors. Since sensor data acquisition and communication are the main sources of power consumption and sensors are battery-powered, an important issue in this context is energy saving during data collection. Hence, the challenge is to extend sensor lifetime by reducing communication cost and computation energy. This paper thoroughly describes the complete design, implementation and validation of the SeReNe framework. Given historical sensor readings, SeReNe discovers energy-saving models to efficiently acquire sensor network data. SeReNe exploits different clustering algorithms to discover spatial and temporal correlations which allow the identification of sets of correlated sensors and sensor data streams. Given clusters of correlated sensors, a subset of representative sensors is selected. Rather than directly querying all network nodes, only the representative sensors are queried by reducing the communication, computation and power costs. Experiments performed on both a real sensor network deployed at the Politecnico di Torino labs and a publicly available dataset from Intel Berkeley Research lab demonstrate the adaptability and the effectiveness of the SeReNe framework in providing energy-saving sensor network models.     

An efficient Lagrangean heuristic for rental vehicle scheduling

In this article we present a heuristic based on Lagrangean relaxation for scheduling rental vehicles. The scheduling problem can be formulated as a set of large assignment problems with linking constraints. We discuss the theory behind the heuristic, including the ability to obtain lower bounds. The heuristic is based on ideas first introduced by D. Wedelin.     

An empirical analysis of ontology-based query expansion for learning resource searches using MERLOT and the Gene ontology

This paper proposes an expansion of queries based on formal domain ontologies in the context of the search for learning resources in repositories. The expansion process uses the relation types that are represented in these models; common ontological relations, and ontological relations specific to domain and traditional terminology relations, typical of thesauri. The tests were conducted using Gene ontology as the knowledge base and MERLOT is used as the test repository. The results of this study case indicate that, at similar levels of precision, expanded queries improve levels of novelty and coverage compared to the original query (without expansion), i.e. expanded queries allow the user to retrieve relevant objects, which might not be obtained without expansion.     

A method for the automatic analysis of the sleep macrostructure in continuum

Sleep staging is one of the most important tasks within the context of sleep studies. For more than 40 years the gold standard to the characterization of patient’s sleep macrostructure has been based on set of rules proposed by Rechtschaffen and Kales and recently modified by the American Academy of Sleep Medicine. Nevertheless the resulting map of sleep, the so-called hypnogram, has several limitations such as its low temporal resolution and the unnatural characterization of sleep through the assignment of discrete sleep states. This study reports an automatic method for the characterization of the structure of the sleep. The main intention is to overcome limitations of epoch-based sleep staging by obtaining a more continuous evolution of the sleep of the patient. The method is based on the use of fuzzy inference in order to avoid binary decisions, provide soft transitions and enable concurrent characterization of the different states. It is proven, in addition, how the new proposed continuous representation can still be used to generate the classical epoch-based hypnogram.     

Accurate multi-robot targeting for keyhole neurosurgery based on external sensor monitoring

Robotics has recently been introduced in surgery to improve intervention accuracy, to reduce invasiveness and to allow new surgical procedures. In this framework, the ROBOCAST system is an optically surveyed multi-robot chain aimed at enhancing the accuracy of surgical probe insertion during keyhole neurosurgery procedures. The system encompasses three robots, connected as a multiple kinematic chain (serial and parallel), totalling 13 degrees of freedom, and it is used to automatically align the probe onto a desired planned trajectory. The probe is then inserted in the brain, towards the planned target, by means of a haptic interface. This paper presents a new iterative targeting approach to be used in surgical robotic navigation, where the multi-robot chain is used to align the surgical probe to the planned pose, and an external sensor is used to decrease the alignment errors. The iterative targeting was tested in an operating room environment using a skull phantom, and the targets were selected on magnetic resonance images. The proposed targeting procedure allows about 0.3 mm to be obtained as the residual median Euclidean distance between the planned and the desired targets, thus satisfying the surgical accuracy requirements (1 mm), due to the resolution of the diffused medical images. The performances proved to be independent of the robot optical sensor calibration accuracy.