Harvesting Ultra-Low Power Wireless Signals in the GHz Range

We present methods for harvesting wireless energy as low as -30 dBm (1uW) from the 2.4 GHz frequency range (e.g. WiFi signals) with discrete components. We have constructed a proof-of-concept device which is capable of operating at -18.8 dBm (13.2 uW) with no onboard power sources, relying solely on the 2.4 GHz energy source. The device is constructed on a PCB and consists of an impedance matching network, a rectifier, and a DC-DC converter. The impedance matching network matches a 2.4 GHz 50 Ohm input source to the high impedance rectifier and provides a passive boost. The rectifier converts the AC signal from the impedance matching network to a DC signal. This DC signal feeds into the DC-DC converter subsystem which boosts the voltage from about 45 mV DC to a clean 95 mV DC output

C4R Project Increases Rail Capacity without Laying Down New Tracks

AbstractRail freight transport is today characterized by inefficiencies in the use of the existing infrastructure while the growing demand is activated by giant containers vessels handling thousands of units in the ports. Lack of industrialization prevents gaining from economies of scale while bottlenecks penalize the optimization of the network use. The rail freight transport market share remains low whereas for environmental reasons immediate progress is required. Capacity4Rail intends to analyze the key factors enabling rail freight market share to increase on the most promising segments.The innovations planned by Capacity4Rail are concentrated on three macro-areas from the concept to simulations and tests: wagon structure design, wagon equipment technology and train maneuverability.For the wagon structure, the project focuses on the new design giving direct efficiency: better payload, less deadweight, extended usable length, maintenance cost reduction. With a reduced weight due to the use of new materials the design evolution allows to make the best use of the gauge profile.For the wagon equipment technology a continuous electric line carrying a bus of information all along the train and bringing energy to the wagon allows placing various sensors increasing safety and reliability. With this new wagon connectivity, predictive maintenance is developed but also accurate real time information are available for the customers enhancing the planning efficiency of the next supply chain evolution. The wagons are equipped with an electric command of the pneumatic brakes for an instant and simultaneous braking and releasing. The brakes of all wagons reduces drastically the longitudinal forces in the couplings enabling progressive lengthening of the train reducing operational costs and network capacity consumption per ton transported.For the train, this new braking system improves its maneuverability, giving access to better paths aiming to reduce the wear of the wheels created by the new brake composite shoes imposed for noise reduction.All these potential progress are researched and checked in terms of affordability taking into account not only the global added value created but an equitable reward of all the stakeholders having invested for such innovations. Proposed roadmaps incorporate viable business models for a progressive implementation on the basis of simulations. A virtuous circle is initiated improving the use of assets, reducing noise, informing customers more efficiently, reducing maintenance and operational costs in an affordable way

Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis

Network traffic analysis, and specifically anomaly and attack detection, call for sophisticated tools relying on a large number of features. Mathematical modeling is extremely difficult, given the ample variety of traffic patterns and the subtle and varied ways that malicious activity can be carried out in a network. We address this problem by exploiting data-driven modeling and computational intelligence techniques. Sequences of packets captured on the communication medium are considered, along with multi-label metadata. Graph-based modeling of the data are introduced, thus resorting to the powerful GRALG approach based on feature information granulation, identification of a representative alphabet, embedding and genetic optimization. The obtained classifier is evaluated both under accuracy and complexity for two different supervised problems and compared with state-of-the-art algorithms. We show that the proposed preprocessing strategy is able to describe higher level relations between data instances in the input domain, thus allowing the algorithms to suitably reconstruct the structure of the input domain itself. Furthermore, the considered Granular Computing approach is able to extract knowledge on multiple semantic levels, thus effectively describing anomalies as subgraphs-based symbols of the whole network graph, in a specific time interval. Interesting performances can thus be achieved in identifying network traffic patterns, in spite of the complexity of the considered traffic classes

Rail freight research: How market trends and customers' needs drive technology innovation

The article presents an investigation of current market trends and customers’ requirements, which have driven research aimed at developing a novel wagon concept that integrates innovative solutions relating to the identified major challenges for the freight vehicles of the future. These challenges are: i. Freight condition monitoring; ii. Lightweight wagon design; and iii. Predictive maintenance. This research was initiated by the INNOWAG project, which is funded by the Shift2Rail Joint Undertaking under the EU’s Horizon 2020 research and innovation programme. The major challenges in rail freight competitiveness relate to the increasing complexity and sophistication of supply chains, increasing transport capacity and logistic capability, as well as improving RAMS and lowering LCC. Therefore, the goal is to develop intelligent cargo monitoring and predictive maintenance solutions integrated on a novel concept of lightweight wagon

Intrusion detection in wi-fi networks by modular and optimized ensemble of classifiers

4noopenWith the breakthrough of pervasive advanced networking infrastructures and paradigms such as 5G and IoT, cybersecurity became an active and crucial field in the last years. Furthermore, machine learning techniques are gaining more and more attention as prospective tools for mining of (possibly malicious) packet traces and automatic synthesis of network intrusion detection systems. In this work, we propose a modular ensemble of classifiers for spotting malicious attacks on Wi-Fi networks. Each classifier in the ensemble is tailored to characterize a given attack class and is individually optimized by means of a genetic algorithm wrapper with the dual goal of hyper-parameters tuning and retaining only relevant features for a specific attack class. Our approach also considers a novel false alarm management procedure thanks to a proper reliability measure formulation. The proposed system has been tested on the well-known AWID dataset, showing performances comparable with other state of the art works both in terms of accuracy and knowledge discovery capabilities. Our system is also characterized by a modular design of the classification model, allowing to include new possible attack classes in an efficient way.openAccademicoGiuseppe Granato; Alessio Martino; Luca Baldini; Antonello RizziGranato, Giuseppe; Martino, Alessio; Baldini, Luca; Rizzi, Antonell

Diverse reductive dehalogenases are associated with Clostridiales-enriched microcosms dechlorinating 1,2-dichloroethane

The achievement of successful biostimulation of active microbiomes for the cleanup of a polluted site is strictly dependent on the knowledge of the key microorganisms equipped with the relevant catabolic genes responsible for the degradation process. In this work, we present the characterization of the bacterial community developed in anaerobic microcosms after biostimulation with the electron donor lactate of groundwater polluted with 1,2-dichloroethane (1,2-DCA). Through a multilevel analysis, we have assessed (i) the structural analysis of the bacterial community; (ii) the identification of putative dehalorespiring bacteria; (iii) the characterization of functional genes encoding for putative 1,2-DCA reductive dehalogenases (RDs). Following the biostimulation treatment, the structure of the bacterial community underwent a notable change of the main phylotypes, with the enrichment of representatives of the order Clostridiales. Through PCR targeting conserved regions within known RD genes, four novel variants of RDs previously associated with the reductive dechlorination of 1,2-DCA were identified in the metagenome of the Clostridiales-dominated bacterial community

Down-regulation of the Lamin A/C in neuroblastoma triggers the expansion of tumor initiating cells

Tumor-initiating cells constitute a population within a tumor mass that shares properties with normal stem cells and is considered responsible for therapy failure in many cancers. We have previously demonstrated that knockdown of the nuclear envelope component Lamin A/C in human neuroblastoma cells inhibits retinoic acid-mediated differentiation and results in a more aggressive phenotype. In addition, Lamin A/C is often lost in advanced tumors and changes in the nuclear envelope composition occur during tumor progression. Based on our previous data and considering that Lamin A/C is expressed in differentiated tissues, we hypothesize that the lack of Lamin A/C could predispose cells toward a stem-like phenotype, thus influencing the development of tumor-initiating cells in neuroblastoma. This paper demonstrates that knockdown of Lamin A/C triggers the development of a tumor-initiating cell population with self-renewing features in human neuroblastoma cells. We also demonstrates that the development of TICs is due to an increased expression of MYCN gene and that in neuroblastoma exists an inverse relationship between LMNA and MYCN expression

Mechanical ventilation and volutrauma: study in vivo of a healthy pig model

Mechanical ventilation is essential in intensive care units. However, it may itself induce lung injury. Current studies are based on rodents, using exceptionally large tidal volumes for very short periods, often after a "priming" pulmonary insult. Our study deepens a clinically relevant large animal model, closely resembling human physiology and the ventilator setting used in clinic settings. Our aim was to evaluate the pathophysiological mechanisms involved in alveolo/capillary barrier damage due to mechanical stress in healthy subjects. We randomly divided 18 pigs (sedated with medetomidine/tiletamine-zolazepam and anesthetised with thiopental sodium) into three groups (n=6): two were mechanically ventilated (tidal volume of 8 or 20 ml/kg), the third breathed spontaneously for 4 hours, then animals were sacrifi ced (thiopental overdose). We analyzed every 30' hemogasanalysis and the main circulatory and respiratory parameters. Matrix gelatinase expression was evaluated on bronchoalveolar lavage fl uid after surgery and before euthanasia. On autoptic samples we performed zymographic analysis of lung, kidney and liver tissues and histological examination of lung. Results evidenced that high V T evoked profound alterations of lung mechanics and structure, although low V T strategy was not devoid of side effects, too. Unexpectedly, also animals that were spontaneously breathing showed a worsening of the respiratory functions