β-cyclodextrin functionalized reduced graphene-silver nanocomposites (β-CD/RGO/Ag) were successfully prepared using two step wet chemical method. The β-CD/RGO sheets were firstly synthesized via hydrazine reduction under 90°C. The Ag nanoparctiles were loaded on the β-CD/RGO sheets by reduction of AgNO3 with ammonia solution. The β-CD/RGO/Ag nanocomposites were well characterized by UV-vis spectroscopy, FTIR, FeSEM and XRD. The results confirmed that β-CD had been effectively covered on the RGO surface and the Ag nanoparticles with an average size of 100 nm were uniformly decorated on the β-CD/RGO sheets. The β-CD/RGO/Ag nanocomposites modified glassy carbon electrode was employed for selective determination of nitrite. Cyclic voltammetry measurements suggested that the β-CD/RGO/Ag exhibits excellent electrochemical activity towards nitrite due to the outstanding electronic properties of RGO sheets and host-guest recognition and enrichment capability of β-CD. The experimental conditions were investigated and optimized. Furthermore, the proposed sensor exhibits excellent anti-interference property acceptable reproducibility.

The Extended Kalman Filter (EKF) has been the state of the art in integrated navigation systems and especially in Pedestrian Dead-Reckoning PDR for foot-mounted Inertial Measurements Units. However in most related work with PDR, indirect filtering approach is used based on linear error Kalman Filter (KF). In this work, it is proposed to outperform this approach by the use of Direct filtering approach, which involves the non-linearity in the propagation of the orientation, velocity and in some models, in position coordinates, where the EKF can not achieve optimal estimation. We propose then, the usage of the most recent algorithms developed in the last decade; Sigma Point Kalman Filters (SPKF), especially based on Cubature rule, called Cubature Kalman Filter (CKF) as the integration algorithm for the inertial measurements. The CKF improves the mean and covariance propagation consequently comparing with EKF and previous SPKF (UKF, CDKF). Although the CKF provides a better estimate of the orientation, velocity and position with Zero velocity UPdaTes (ZUPT) measurements and Zero Angular Rate UpdaTes (ZARUT), additional sensors are necessary to measure other states such as yaw angle and to estimate properly the gyroscope bias. We studied then the possibility to integrate electronic compass as additional measure and also Map street data base or Map building data base depending on the type of navigation "Indoor", "Outdoor". In order to get much better estimation based on the Cubature rule, it is proposed to synthesize CKF in order to get robust estimation against nonlinearity of the process and the multiple measurement sensors.

Coffee aroma is considered to be one of the most complicated food aromas which have more than 600 components. For the analyses of total aromas objectively, some electronic noses have been applied and succeed in distinguishing coffees. However, the feature of coffee aroma (what kind of smell) was not clear, because the results from electronic noses showed generally the values in resistance/current, or distinguish the samples using principal component analyses. Here we present FF-2A electronic nose (Shimadzu Corporation, Japan) can distinguish and describe coffee aromas like wine experts, comparing with the smells of wine aromas kit, "Le Nez du Vin." Our results showed the electronic nose distinguished and described aromas from 2 types of Colombia coffees, instant coffee solution with 90˚C water and brewed coffee in a coffee shop (Fujioka K et al., Jpn J Taste Smell Res, 2013). The analysis data comparison with 51 aromas suggested the aroma from the brewed coffee was similar to Champignon, Pine, Coffee flavour, Honey, Strawberry, Musk, and Caramel. On the other hand, the aroma from the instant coffee was similar to Coffee flavour only. In conclusion, the electronic nose which learned "Le Nez du Vin" wine aroma kit distinguished and described the aroma from the coffees. As novel data, we also present aroma analyses of canned coffees. Since our method is applicable to other electronic noses, it will expand utilization of electronic noses.

In this paper, we describe the techniques and technologies used in our home automation system based on 6LowPan. This IP-based protocol has the advantage that no extra layer or logic is required for communication with a node in or outside the network. The 6LoWPAN network is divided into three main parts: the central server, the edge-routers and the embedded motes. The central server is located at the top-most level and keeps track of the complete network status. This server allows users to request the status and to administer system changes. It runs a database, which holds the necessary information about the complete network.On the bottom-most level one can find the embedded Zolertia motes. These motes can measure physical entities (i.e. temperature, humidity, etc.) and/ or toggle the status of attached devices (i.e. lighting, heating, etc.). In between one can find the edge-routers, formed by combining a Zolertia mote together with a Beaglebone. The edge routers allow the data to bridge the compressed IPv6 embedded wireless network and the regular IPv6 network. Dedicated IPv6/IPv4 tunneling ensures communication between the central server and the beagle bones.

Biosensor is an analytical device that consists of immobilized biological sensitive materials. When these materials are in contact with certain transducers, the sensor is able to convert biological signal into electrical signal, hence allowing for certain measurement to be conducted. These sensors have the capability to detect certain human traits such as DNA, tissues, enzyme, antibody and antigen. To increase the biosensor performance especially the interaction between the sensor and biological elements, high uniformity and good optical transmittance sensors are strongly important. Therefore, this paper will presents early characterization of biosensors using Zinc Oxide (ZnO) piezoelectric thin film deposited as sensing layer on Silicon substrate. We investigated the thin film surface morphology and optical characterization using Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM), and UV-Visible Spectrophotometer. We found that the surface roughness of the thin film varied from 1.1 nm to 4.5 nm and the grain size increased with the increase of annealing temperature thus provide high surface uniformity that will enhance the sensitivity and specificity of the sensor.

Fiber Bragg Gratings (FBGs) are one of the most widespread types of fiber sensors for measuring temperature and deformations because of their robustness and sensitivity, combined with the possibility of multiplexing to form complex multipoint sensing systems. Traditional interrogation techniques - especially in the case of multiple sensors - are based on broadband sources and spectrum analyzers; this solution, although effective, presents however two weaknesses: resolution (at least for not too expensive devices) and time necessary for the spectral scanning. Recently fast spectrometers have been introduced in the market, but their resolution is still limited to about 100 pm, which corresponds to about 10 °C when used for temperature sensing.The paper presents a fast and high resolution temperature sensing system based on such fast spectrum analyzers and critically assesses its performance. The resolution has been improved by using a spectral fitting method with Gaussian functions and a resolution of 0.18 °C has been achieved. On the other hand, the time response depends not only on the interrogation system but also on the sensor itself so, in the proposed setup, bare FBG sensors are used instead of more common FBG temperature sensors for their reduced footprint that allows point measurements and very small time constant, comparable with thermocouples. The temperature behavior of bare FBGs , however, is typically not provided with the required accuracy by FBG manufacturer; moreover they exhibit a strong cross-sensitivity with strain, so a proper preliminary characterization is necessary before their practical application. This has been carried out with an environmental chamber and an acquisition and processing system specifically developed to manage fast spectrum analyzers. Given its performance, the potential applications of the proposed sensing system are mainly in the medical field where a non-metallic temperature probe with small dimensions and fast response is required, like in measuring temperature during thermal ablation of cells.

In the design of Lorentz force MEMS magnetometers, the coupled thermo-electro-magneto-mechanical fields governing the dynamics of the relevant compliant structures can be appropriately exploited to enhance their performances. In recent works, we showed that reduced-order models for the dynamics of the said movable structures can be recast in the form of theDuffingequation, where nonlinear terms arise from the multi-physics governing the problem. As stochastic effects may play a role due to the micrometric dimensions of the device, an investigation of the link between the statistics of sensor imperfections and output is here carried out. The said imperfections at the microscopic length-scale are modeled in terms of:overetch thickness, assumed to feature a uniform distribution in a proper interval matching available experimental data; and elastic properties of the vibrating polycrystalline silicon film, as obtained through a numerical homogenization procedure over a representative film volume. To get insights into the effects of the parameters governing the nonlinear dynamics of the resonant structure, a Monte Carlo analysis is adopted. In the design of Lorentz force MEMS magnetometers, the coupled thermo-electro-magneto-mechanical fields governing the dynamics of the relevant compliant structures can be appropriately exploited to enhance their performances. In recent works, we showed that reduced-order models for the dynamics of the said movable structures can be recast in the form of theDuffingequation, where nonlinear terms arise from the multi-physics governing the problem. As stochastic effects may play a role due to the micrometric dimensions of the device, an investigation of the link between the statistics of sensor imperfections and output is here carried out. The said imperfections at the microscopic length-scale are modeled in terms of:overetch thickness, assumed to feature a uniform distribution in a proper interval matching available experimental data; and elastic properties of the vibrating polycrystalline silicon film, as obtained through a numerical homogenization procedure over a representative film volume. To get insights into the effects of the parameters governing the nonlinear dynamics of the resonant structure, a Monte Carlo analysis is adopted.

Investigating novel applications of conventional media for biosensor applications resides prospective developments in health-care services.  As an example, colorimetric test strips are widely renowned tools utilizing rather simple measurement media and detection principles.  Similarly, optically transparent media can be used as cheap, consumable, readily available, small sample and technical expertise requiring materials that can be combined with basic detection principles, which are applying spectrophotometric measurements, as essential methods of many laboratories.  For this purpose, cellulose dialysis tubing (dialysis membrane) is investigated for measurements of dry biological sample spots with UV spectroscopy, with the bovine serum albumin protein as the sample of interest.  Dialysis membranes are among the common media in biological research and biosensor applications, but they have not been studied up to now as the chief medium for sample adsorption or immobilization, for the following detection.  Hence this ongoing research focuses on exploring the feasibility of dialysis membrane as a novel platform for biological testing, by determining the peaks, peak positions, and peak shifts, with respect to the pure protein measurements in solution, along with estimating the optimal sample spot dimensions, for improving the results and reproducibility.  Also, the approach is promising to be developed as a point-of-care diagnosis tool since the necessary instruments of the proposed biosensor are appreciably miniaturized and can be listed among the mobile phone utilities in the future.

One of the main problems associated with the use of adaptive filtering for noise cancellation is the nature of the noise signals. This problem imposes the use of high complexity algorithms to reduce the noise in useful signals. This can be impractical for many real time applications, where computational power is a critical issue. Most of existing literature approaches is based on a single and usually complex adaptation algorithm to do the job. In this paper, a new mechanism is devised to eliminate background noise from speech communications. The procedure is based on a two-sensor adaptive noise canceller that able to assign a suitable algorithm according to properties of the noise. The criterion used here is based on calculating the eigenvalue spread of the autocorrelation of the input noise. The new smart noise canceller (SNC) applies a suitable adaptive algorithm according to the eigenvalue spread. This approach showed its capability in executing noise cancellation under different types of environmental noise. Fast convergence rates, improvement in signal-to-noise ratio and substantial reduction in computational power are obtained using this SNC technique. Experiments are conducted using real life signals to demonstrate the success of the method.

Smart Grid is a recent area where the key feature is shift the present power system approach. But, the challenges of upgrade this present power system are several, such as: how to add reliable links between customers' home and data centers to enable smart meter sending power consumption data? how to avoid big data and bottleneck on backbone to transmission of millions of these customers' devices? On the other hand, smart meter can be treated as a sensor network device. Thus, it can use the same data reduction mechanisms that have been studied in wireless sensor network to decrease its traffic. This paper proposes a data reduction approach based on prediction by simple linear regression to avoid flow of readings between smart meter and smart grid system. Although the approximation performed by linear regression increases the prediction error in some instances, we have implemented an adaptive mechanism (Adaptive Simple Linear Regression - ASLR) that checks if the prediction error or lack of relationship between the modeled samples is harmful to our data reduction approach. Thereby, two ways have been deployed to tune the samples window (amount of readings) for improve own approach. These samples are smart meter readings which are modeled in a linear regression function for recovering data instead of sending it to the datacenter. One mechanism adjusts samples window based on prediction error and another one adjusts samples window based on Pearson's coefficient. Also, some experiments were conducted using the Wavelet as mechanisms for data reduction but the best results were obtained using ASLR, which saves on the data transmission to a controllable level of error. This article contains all steps to reach at a more robust solution, which serves as a guide to others as lessons learned.