In this work, the performance of ISM 2.4GHz Wireless Sensor Networks (WSN) deployed in Judo training venues is analyzed. Judo is a very popular martial art worldwide, which is practiced by thousands of people not only at competition level, but also as part of physical education programs at different school levels. There is a great variety of Judo training venues, as each one has specific morphological aspects, making them unique complex indoor scenarios in terms of radiopropagation due to the presence of furniture, columns and equipment, and the presence of human beings, which is a major issue as the person density within this kind of scenarios could be high. Another key aspect is the electromagnetic interference created by other wireless devices, such as personal portable devices and other wireless systems such as WiFi or other WSNs, which make the radioplaning a complex task in terms of coexistence. In order to analyze the impact of these features on the radiopropagation and the performance of WSNs, an in-house developed 3D Ray Launching algorithm has been used. The obtained simulation results have been validated with a measurement campaign carried out in the sport facilities of the Public University of Navarre, by means of ZigBee-compliant XBee-Pro modules. The analysis is completed with the inclusion of an in-house human body computational model. The presented analysis can aid in obtaining the optimal network configuration and performance in terms of energy efficiency and capacity, making the use of WSNs attractive for multiple applications in Judo training venues.

Contemporary water distribution networks exploit information communication technologies (ICT) to monitor and control the behavior of water network assets. Limited capability and typically battery powered low-resourced devices, such as smart meters/sensors, have been used to transfer information from the water network to data centers for further analysis. Many water companies deploy devices aiming to last beyond the 10-year mark. This prohibits the use of high-sample rate sensing therefore limiting the knowledge we can obtain from this data. However, data reduction techniques with minimal information loss can overcome this problem. In this paper, we present a self-adaptive scheme that reduces the amount of transmitted data, thus extending the battery life of sensor nodes, while still maximizing the received information to data centers. To achieve these goals, we exploit the power of compressive sensing (CS), which enables significant compaction of the original information content in a few random incoherent projections. Sparsity of the recorded data streams, which is a necessary condition for successful CS reconstruction, is achieved via the transformation of the original data into an appropriate transform domain. Using over 170 days of real high-sample rate water pressure data from 25 sensor nodes of our large scale testbed in Bristol area, we verify the efficiency of our CS-based algorithm in reducing significantly the data volume, and thus extending the battery life of sensor nodes. In addition, we demonstrate that our system supports self-tuning and automatic reconfiguration as the nature of incoming data changes over time.

This paper discusses the user and technical requirements in designing smart garments for biomedical monitoring in several and very different applications: in hospital settings, during activities of daily living (ADL), sport and fitness, home care, working environments.

Anthropometric and gender considerations are to be included into design as well as textile requirements like elasticity, washability and chemical agents effects for preserving sensors' efficacy and reliability, and assuring the proper duration of the product for the complete life cycle.

Phisiological issues are mainly due to skin conductance (and related operations: cleaning, scrubbing the external layer of dead skin cells, the presence of hair - expecially in male subjects), skin tolerance and irritation, and the effect of sweat and perspiration.

All these factors strongly affect the design and technical choices (materials in particular) but aesthetical requirements are proved to be crucial as well as.

For this aspect, user's age, target application, and fashiontrend could not be ignored, because they determine the final success of the wearable monitoring approach.

Water bodies are essential to humans and other forms of life. Identification of such water bodies can be useful in various ways: estimation of water availability, demarcation of flooded regions and so on. In past decades, Landsat sensors have been used for land use classification using various unsupervised and supervise methods. With the introduction of new OLI sensor in Landsat 8 with improved qualities, the accuracy of classification has been much improved. With increasing quality, the data size are also increasing, at the same time data mining techniques are developed to improve the classification efficiencies. The objective of the study is to apply J48 decision tree to identify water bodies using Landsat 8 OLI imageries. J48 is an open source java implementation of C4.5 decision tree. The imagery for the study is from Chuncehon, Republic of Korea area. Training data with individual bands and band ratios were used to develop the decision tree model and later applied to the whole study area. The performance of the result was statically analyzed using Kappa statistics and Area under Curve. The result shows a successful application of data mining technique in robust water body identification.

Development of new technologies, particularly the Internet and Sensor Networks, creates a completely new paradigm of the Internet utilization, commonly known as “The Internet of Things (IoT)”. Interconnected sensor-based systems are a key enabler in the realization of an IoT vision. Therefore, the IoT can be defined as a worldwide network of “smart things” enabled to interact and communicate to each other, as well as with the environment, empowering better understanding of the “real/physical world” and discovering and extracting information about objects and actions that drive that world. By populating the environment with real-world sensor-based devices, the IoT is opening the door to exciting possibilities for a variety of novel applications. Considering that sensor nodes today can be looked as smart objects, they can produce significant computational power which can be used for manipulating and processing collected information. Therefore, computational power increases cost per unit, and what is more important raises energy consumption which is still a primary limiting factor. Additionally, energy consumption is increased by execution of complex algorithms, and in the process of design and building a smart information system it is crucial to choose the optimal location (sensor nodes or remote processing unit) for implementation and execution of the computational logic. Considering that energy and power efficiency are essential factors in the design and operation of sensor nodes and that there are a number of initiatives and tendencies to improve the power efficiency in variety of areas, analysis of this concept in the sensor nodes is shown as very interesting.

Relying on the fact that the choice of control algorithm and location of the computational logic may strongly influence power efficiency, a prototype sensor node, empowered by using fuzzy logic in decision making process, is built and tested in real case environment scenario. Used fuzzy logic processing algorithm is based on predefined rules and can detect a temperature changes in order to ensure accurate and timely response in the case of fire presence. Comparative analysis of power efficiency has been done, and was carried out for best, worst and average case of timely depended temperature changes. The aim of the experiment is to show which solution is the optimal in the sense of energy consumption – implementation of computational logic on sensor node or on a remote host. Regard to this, transmitted and received power, voltage supply and minimum of required voltage level are essential factors for correct operation. In addition, the valuable factor, primarily for the network supply nodes, is power quality improvement in the energy network of nodes due to the relocation of control logic.

The substrate integrated waveguide (SIW) is an emergent technology that allows the fabrication of millimeter and microwave components and subsystems with the advantages of conventional metal waveguides, but being lighter and of easy integration of planar components and circuits . SIW is normally produced by creating rows of metallized holes using conductive cylinders or slots, which are embedded in the dielectric substrate and connect the two parallel metal plates. Some authors have presented the integration of SIW technology in new materials, such as textiles, for the fabrication of microwave components. In these works the metallized holes are made with rigid brass eyelets which already provide some flexibility, very important for further integration of the SIW components into garments. Taking this into account this paper presents the state of art about developing flexible SIW namely on textile substrates. Then, conductive materials, as for instance threads, tapes and knit strings are applied through handmade techniques to produce SIW in transmission lines. The results are then compared to transmission lines with SIW made with brass eyelet. Then, SIW antennas were also developed. All developed SIW probes were tested with a Vector Network Analyzer to measure the S11 and S21 parameters. The obtained results prove that the use of conductive threads through embroidery technique allows the implementation of more flexible SIW in textile substrates, with similar efficiency to the ones made with rigid eyelets currently in use. This technique can thus create new possibilities for the development of SIW textile antennas and components.

        Aluminum doped titanium nitride–silicon oxide–hafnium oxide–silicon oxide–silicon device with Fluorine plasma treatment (hereafter F-Al-MOHOS) can be a candidate for total ionization dose (TID) radiation sensor application. In this report, the performance improvement in terms of gamma TID radiation induced charge generation effect and charge-retention characterization for F-Al-MOHOS (SONOS-type Hf-based high K device) is the main subject of this study. F-Al-MOHOS devices with various Aluminum compositions in the HfO₂ charge-trapping layer tuned by metal organic chemical vapor deposition (MOCVD) system and with various Fluorine plasma treatment deposited by plasma-enhanced chemical vapor deposition (PECVD) system are to be used for comparison.

       Results indicate that better TID radiation induced charging effect is achieved with 20% Al content in the HfO₂ trapping layer in this study. The radiation induced trap density of the HfO₂ trapping layer can be increased by tuning a suitable Al ratio in HfO₂. But the charge retention performance of Al-MOHOS can be increased by increasing the Al doping ratio. Doping high ratio Al into pure HfO₂ film can enhance the crystallization temperature of Al-HfO₂ compound and improve the charge retention characteristic of Al-MOHOS device with high temperature S/D annealing process [1]. However, due to the fluorine incorporation into HfO₂ trapping layer by F plasma treatment pre and post HfO₂ deposition (hereafter pre post F treatment), radiation induced charge generation efficiency for F-MOHOS is also enhanced. Meanwhile, the charge-retention characteristic of F plasma treatment MOHOS device has also been significantly improved. It shows that the charge-retention performance of the F-MOHOS device with negative gate bias stress (NVS) after 10Krad TID radiation exposure, the condition of pre-HfO₂ F plasma treatment one is better than the post-HfO₂ F treatment condition. But the charge-retention performance of the F-MOHOS device with NVS for 5Mrad TID radiation exposure condition, the post-HfO₂ F plasma treatment is better than pre-treatment condition. The result obviously indicates that F plasma treatment is helpful to passivate the HfO₂-SiO₂ interface, eliminate shallow trap effectively and result in deep charge trap level [2] .

     The experimental results show that radiation induced charge density of F-Al-MOHOS device with 20% Al doping  and pre post F treatment HfO₂ is 6 times larger than that of MONOS device. In brief, the significant improvements in terms of radiation induced charging effect and charge-retention characterization of F-Al-MOHOS device may be achieved by doping suitable Al content and pre post F passivation for the HfO₂ charge-trapping layer, which can be attributed to the radiation induced high density deep trapped charges for F-Al-MOHOS radiation sensor device. The F-Al-MOHOS reported in this study has demonstrated their potential application for non-volatile TID radiation sensing application in the future.

For many diseases, where a particular organ is affected, chemical by-products are found in the patient’s exhaled breath. Breath analysis is often done using gas chromatography and mass spectrometry, but interpretation of results is difficult and time-consuming. We performed characterization of patients’ exhaled breath samples by an electronic nose technique based on an array of nanomechanical membrane sensors. Each membrane is coated with a different polymer layer. By pumping the exhaled breath into a measurement chamber, volatile organic compounds present in patients’ breath diffuse into the polymer layers and deform the membranes by changes in surface stress. The bending of the membranes is measured via four piezoresistive bridges at the fixation points of the membrane. The resistance changes in the Wheatstone bridge are converted in an electronic circuit to voltages and are recorded. The sensor deflection pattern allows to characterize the condition of the patient. In a clinical study, we investigated breath samples from head & neck cancer patients and healthy persons. Evaluation using principal component analysis (PCA) allowed clear distinction between the two groups. As head & neck cancer can be completely removed by surgery, the breath of cured patients was investigated after surgery again and the results were very similar to those of the healthy control group indicating that surgery was successful.

Partial discharges are ionization processes inside or on the surface of dielectrics that can unveil insulation problems in electrical equipment. The charge accumulated in the dielectric is released under certain environmental and voltage conditions attacking the insulation both physically and chemically. The final consequence of a continuous occurrence of these events is the breakdown of the dielectric. The electron avalanche provokes a derivative of the electric field close to the damaged insulation creating an electromagnetic impulse that can be detected with antennas. The localization of the source of partial discharges helps in the identification of the piece of equipment that has to be decommissioned. This can be done deploying antennas in open-air substations and calculating the time difference of arrival (TDOA) of the electromagnetic pulses. This parameter is critical in the localization and small errors can lead to a great displacement of the calculated position of the source. Usually, four antennas are used to find the source in space but the array has to be correctly deployed to have minimal errors in the localization. This paper demonstrates theoretically and experimentally that the most common layouts are not the best option and proposes a simple antenna layout to reduce the systematic error in the TDOA calculation due to the positions of the antennas.

Sparse autoencoders are used to extract important features that can be used in classification or regression applications. In this paper we present a novel sparse autoencoder for modeling high-dimensional sensory data that allows the user to set the sparsity level and can be used for both off-line or on-line learning applications. The encoder starts by generating random basis functions and adjusts the parameters of the basis functions as data arrives for training. After training a sensory data can be represented by a linear combination of a few number of basis functions. Unlike other
autoencoders our sparse encoder does not require special preprocessing of the sensory data. Potential applications of the autoencoder among others include the realization of advanced feature detectors and signal processing methods. We evaluated the performance of the method on standard image data from the literature and found that our autoencoder gives results comparable to the results reported in the literature.