With the eye-catching advances in sensing technologies, smart water networks have been attracting immense research interest in recent years. One of the most overarching tasks in smart water network management is the reduction of water loss (such as leaks and bursts in a pipe network). In this paper, we propose an efficient scheme to position water loss event based on water network topology. The state-of-the-art approach to this problem, however, utilizes the limited topology information of the water network, that is, only one single shortest path between two sensor locations. Consequently, the accuracy of positioning water loss events is still less desirable. To resolve this problem, our scheme consists of two key ingredients: First, we design a novel graph topology-based measure, which can recursively quantify the "average distances" for all pairs of senor locations simultaneously in a water network. This measure will substantially improve the accuracy of our positioning strategy, by capturing the entire water network topology information between every two sensor locations, yet without any sacrifice of computational efficiency. Then, we devise an efficient search algorithm that combines the "average distances" with the difference in the arrival times of the pressure variations detected at sensor locations. The viable experimental evaluations on real-world test bed (WaterWiSe@SG) demonstrate that our proposed positioning scheme can identify water loss event more accurately than the best-known competitor.

Currently there is a great inclination to modify sport and well-being concept by changing the technology in wearable especially thanks to the huge development of technologies in the field of smart textiles. Textiles of today are materials with applications in almost all our activities. Fibres, yarns, fabric and other structures with added-value functionality have been developed for a range of applications textile materials and the textile has become an important platform for high-tech innovations. Smart Textile creates textile products that interact by combining smart materials and integrated computing power into textile applications. The introduction of smart materials and computing technology in textile structures offers an opportunity to develop textiles with a new type of behavior and functionality. Smart Textile and computing technology are introducing a shift in textile, from a passive to a dynamic behavior, from textiles with static functionalities to products that exhibit dynamic functionalities. This work will describe the results of an educational activity carried out inside the Sportswear Studio Lab of Master Degree in Fashion Study at School of Design of Politecnico di Milano. The students were asked to generate a new advanced concept for sport application exploiting the potentiality of smart textile and wearable technology. The projects developed by the students followed a design approach suggested by the author that requires the understanding of: (i) the what (the purpose of the concept); (ii) the how (the used technology); (iii) the where (the context in which the product is used) and finally (iv) the wearability issues connected to the role of technology in human body changing and perception.

Modern and sustainable viticulture entails objective and fast monitoring of crucial variables for rational decision making. The development of new, non-invasive technologies in the last decade has enabled the acquisition of large amount of data from the vineyard, which need to be properly analysed to provide helpful information to viticulturists. In this context, data mining strategies may be applied to agricultural data, with the aim of yielding useful, reliable and objective information. This work presents the most recent applications of machine learning algorithms to grapevine plant phenotyping, specifically to variety discrimination, and assessment of plant water status. Support vector machine (SVM) and modified partial least squares (MPLS) models were built using NIR spectra acquired in the vineyard, on grapevine leaves, with a portable spectrophotometer working on the spectral range between 1600 to 2500 nm. Spectral measurements were acquired on the adaxial side of 200 individual leaves (20 leaves per cultivar) of ten (Vitis vinifera L.) varieties. Sequential minimal optimization (SMO) algorithm was used for the training of a SVM for varietal classification. The classifier’s performance for the 10 varieties surpassed the 94.9% mark. For water stress assessment, the predictive model based on MPLS using the reflectance spectra of four cultivars, and the first and second derivative, yielded a R²= 0.81 for stem water potential (y_s), which is widely recognized as an integrative indicator of whole-vine water status, but destructive and very laborious. These results show the power of the combined use of data mining and non-invasive sensing for grapevine phenotyping and their usefulness for the wine industry.

Most of the gas sensors are based on resistors with inorganic materials and more rarely on other conductometric devices (diodes or transistors). Conductometric sensors have also been designed with molecular materials. Thus, in 2009, Molecular Semiconductor — Doped-insulator (MSDI) heterojunctions were built around a heterojunction between a molecular semiconductor (MS) and a doped-insulator (DI). The MS must be more conductive than the sublayer to take advantage of the heterojunction. The MS is generally of p-type and DI can be of p-type (p-MSDI) or n-type (n-MSDI) material. The energy barrier at the interface depends on the difference in the charge carrier density in the two layers, leading to a variable extent of the plateau in the current-voltage characteristics, according to materials. We use this new transducer to detect different gases. We studied, in particular, the response of MSDIs to ammonia in a broad range of relative humidity (rh). A n-MSDI exhibits a positive response to ammonia (electron donating species) and a negative response to ozone (oxidizing species). Whereas the only material in contact with the gas is a p-type MS (LuPc₂), these responses are opposite to those of a resistor prepared by only this material (LuPc₂). The better stability toward humidity was obtained with Cu(F₁₆Pc) as n-type sublayer. Thus, for Cu(F₁₆Pc)/LuPc₂ MSDI (50 nm/50 nm), the relative response to NH₃ is almost not affected by the variation of rh in the 20-80 % range.

In the context of Wireless Body Sensor Networks (WBSN) for healthcare and pervasive applications, the wearable antennas offer the possibility of ubiquitous monitoring, communication, energy harvesting and storage. Textile antennas are the link for a non-invasive integration of communication equipment and sensors, boosting the garment as an interface that extends the communication system.

The integration of electronic devices on clothing begs the question about how to feed them. The batteries are an obvious choice, but they are bulk, require frequent replacement or recharging and their finite lifetime has become a major ecological concern over the past years. Therefore, energy harvesting holds a promising future in the next generation of WBSN, where it will be possible to feed all its nodes without the need of replacement of batteries, thus creating auto-sustainable systems.

Nowadays, as radio frequency energy is currently broadcasted from billions of radio transmitters (e.g. mobile communications and television/radio stations) it can be collected from the ambient. Responding to this context, this paper presents a smart coat with an embedded dual-band textile antenna for electromagnetic energy harvesting, operating at GSM 900 and DSC 1800 bands. The results obtained before and after the integration of the antenna into the garment are compared. Also, the influence of the user’s body in the antenna performance is analyzed. In free-space measurements this antenna shows 2dBi gain and efficiency 80%. The integration of textile antennas into smart clothing emerges as a particularly interesting solution when the replacement of batteries is not easy to practice.

Motion capture is a powerful tool used in a large range of applications towards human movement analysis. Although it is a well-established technique, its main limitation is the lack of dynamic information such as forces and torques during the motion capture. In this paper, we present a novel approach for human wearable dynamic (WearDY) motion capture for the simultaneous estimation of whole-body forces along with the motion. Our conceptual framework encompasses traditional passive markers based methods, inertial and contact force sensor modalities and harnesses a probabilistic computa- tional framework for estimating dynamic quantities originally proposed in the domain of humanoid robot control. We present experimental analysis of our framework on subjects performing a two degrees-of-freedom bowing task and we estimate the motion and dynamic quantities. The results demonstrate the validity of the proposed method. We discuss the implications of our proposal towards the design of a novel wearable force and motion capture suit and its applications.