Over the last few decades, optical sensing and imaging have attracted much attention in biomedical applications such as near-infrared spectroscopy [10], photoacoustic microscopy [11,12], nonlinear microscopy [13,14], and optical coherence tomography (OCT) [15,16]. Compared with other optical imaging techniques, OCT has the advantages of deeper imaging depth, requiring no contrast agents, and high imaging speed. Based on the interferometer configuration, either two-dimensional or three-dimensional micro-structural information can be reconstructed without destroying the sample. Since 1991, many research groups have demonstrated that OCT can be applied in various biomedical fields such as ophthalmology, dermatology, and oncology [17�C19].

In the last decade, the imaging speed and system sensitivity have been greatly improved due to the development of Fourier-domain OCT (FD-OCT) without mechanical scanning in the reference arm of the interferometer. Furthermore, FD-OCT includes two different configurations known as swept-source OCT (SS-OCT) [20�C22] and spectral-domain OCT (SD-OCT) [23�C25]. Aside from obtaining structural information, OCT can perform functional imaging including tissue birefringence, blood flow velocity and angiography [26�C28].Many dermatological studies using OCT have been reported [29�C35], most of which focus on the detection of pathological changes in the skin due to skin disorders. Additionally, dermal birefringence, which can be utilized for the diagnosis of sun damage [33] or for the determination of burn depth [34], can be visualized using polarization-sensitive optical coherence tomography (PS-OCT).

Furthermore, Yasuno et al. were able to differentiate young and old photo-aged human skin based on a birefringence analysis using PS-OCT [35]. In addition to characterizing skin morphology, OCT has been proposed Carfilzomib by Ohmi et al. as a tool for performing dynamic analysis of mental sweating from human fingertips [36]. The same group was also able to visualize the dynamics of the small arteries and veins of human fingers using OCT [37].In this study, an SS-OCT system is implemented for the investigation of moisture-related optical property of human skin. In our experiments, OCT scans taken every 3 min after soaking the palm in water were used to observe water diffusion and evaluate the moisture-related attenuation coefficient of human skin.

The time-resolved OCT scans revealed the process of water diffusion in the skin, which we then analyzed quantitatively along with the skin’s moisture by evaluating the skin’s attenuation coefficients. Then, the OCT scanning results were compared with the measurements made by a commercial moisture monitor. Furthermore, to investigate the diffusion velocity in skin, the positions of center-of-mass of backscattered intensities in the longitudinal direction from OCT images are evaluated.2.

In this sense, this work differs from existing solutions in at least three aspects: (i) it employs an intelligent method to monitor electric energy consumption; (ii) it uses ML techniques to analyze the behavior of electronic equipment by means of the WSNs; and (iii) it sends alerts to a smartphone in an intelligent way when an anomaly is identified.To validate the performance of the NodePM, we built a prototype (see Section 3) to scan the power consumption of the electronic equipment. A number of experiments were conducted with this prototype, including an analysis of variance (ANOVA) and parametric and non-parametric tests. The results of these tests, which were obtained from a statistical analysis, provided evidence of the feasibility of the NodePM in the platform that was developed.

In addition, the NodePM provided better efficiency to detect novelties when compared with the Self-Organizing Novelty Detection (SONDE) [16] method, outperforming it in different scenarios in all evaluations carried out.The rest of this paper is structured in the following way. Section 2 reviews related studies. Section 3 describes the strategy employed for the development of the work and the method proposed. Sections 4 and 5 provide an assessment of the performance and include a discussion of the results obtained. Finally, Section 6 shows the conclusions and examines what contribution has been made by this study.2.?Related WorkSeveral studies have been published in the area of smart grids in the last few years. This section presents scientific papers [3,5,6,8,9,11,12] whose focus corresponds mostly to the monitoring of electric energy consumption.

However, despite all the recent advances that have been made, there are still a number of challenges and unresolved problems in this area. For example, the lack of a methodology to detect anomalies/novelties in the monitored environment.One of the oldest models of a smart grid is the Telegestore project [6]. Telegestore is a system Batimastat for the remote management of residential and commercial meters with a view toward exploring the low-voltage distribution system between the transformers and meters. The main limitations of Telegestore are as follows: (i) the disregard of a method for detecting anomalies (for example, black-outs) in the low voltage system; and (ii) the monitoring of the sectors is not examined, such as a room in a house, for example.Erol-Kantarci and Mouftah [5] propose the use of a WSN to manage the electricity of a residence by means of the smart grid. For this reason, researchers have proposed a work, called Appliance Coordination (ACORD), to reduce the cost of energy at peak times.

This approach is closer to our work, since a cloud-based cooperative MAC protocol is proposed and evaluated. However, there are two key differences with respect to our proposal: (i) RLNC is not considered, and relays transmit the same uncoded packets simultaneously, thus creating virtual multiple-input-multiple-output (MIMO) links; and (ii) the role of the relay cloud is fundamentally different. In particular, each relay in the cloud decides in a distributed way whether to participate in a cooperation phase based on a transmission probability
Energy harvesting or energy scavenging is the process of extracting small amount of energy from ambient environment through various sources of energy. The available energy for harvesting is mainly provided by ambient light (artificial and natural lighting), ambient radio frequency, thermal sources and mechanical sources.

Reduction in size and energetic demands of sensors, and the low power consumption trend in CMOS electronic circuitry opened novel research lines on battery recharge via available power sources. Harvesters can be employed as battery rechargers in various environments, such as industries, houses [1,2], the military (as for unmanned aerial vehicles [3]) and handheld or wearable devices [4�C9]. The possibility to avoid replacing exhausted batteries is highly attractive for wireless networks (Wireless Sensor Networks [10]), in which the maintenance costs due to battery check and replacement are relevant. Another emerging field of application is biomedical systems, where the energy could be harvested from an off-the-shelf piezoelectric unit and used to implement drug delivery systems [11] or tactile sensors [12�C14].

Recent research also includes energy conversion from the occlusal contact during chewing by means of a piezoelectric layer [11,15] and from heart beats [16].We can classify the main energy harvesting technologies by the hierarchy shown in Figure 1. Motion harvester systems can be structured as follows: GSK-3 the harvester collects inputs through its frame, directly connected to the hosting structure and to the transducer; at the end of the system chain, a conditioning circuit manipulates the electrical signals. This paper specifically focuses on piezoelectric motion harvesting techniques.Figure 1.Hierarchy of main energy harvesting technologies.

The possibility and the effectiveness of extracting energy from human activities has been under study for years [17]. As a matter of fact, continuous and uninterrupted power can potentially be available: from typing (~mW), motion of upper limbs (~10 mW), air exhalation while breathing (~100 mW), walking (~W) [18,19] (Figure 2), and in this work we review state of the art of motion based energy harvesting.Figure 2.Estimation of available power that could be harvested during human activities (Adapted from [22]).

The nodal loads of each film element are the work balance of the body forces F in volume V, the surface tractions �� on surface S, the strain ��0, and the initial stressg����. The element load vector is:re=��[N]TFdV+��[N]T��dS+��[B]TE��0dV?��[B]Tg����dV(5)where [N], [B], and E are the shape function matrix, the strain-displacement matrix, and the material property matrix, respectively [21].In general, the SAM orientations on the gold surface are complex and induced surface stresses from the SAM adsorption are anisotropic [22�C24]. Here, we assume that SAM domains are randomly dispersed on the surface (as shown in Figure 3a) and oriented according to an arbitrary angle (as shown in Figure
The transfer of oxygen from the lungs to the tissue cells is largely carried out by the hemoglobin molecules in the red blood cells and only 2% of the total oxygen content is dissolved in the plasma.

Oxygen saturation in the blood is the ratio of oxygenated hemoglobin concentration to total hemoglobin concentration in the blood, and its value in the arterial blood, SaO2, is of great clinical and physiological significance since it reflects the adequacy of oxygen delivery and respiratory function. Normal values of SaO2 are 94%�C98% at sea level but the values may decline somewhat beyond the age of 70 years [1].SaO2 can be assessed in vitro, in extracted arterial blood, either directly by means of co-oximetry or by measuring oxygen partial pressure and using the oxygen-hemoglobin dissociation curve.

Estimation of SaO2 can also be obtained non-invasively Anacetrapib by pulse oximetry [2�C4], which is based on the different light absorption spectra for oxygenated and de-oxygenated hemoglobin (Figure 1). In order to assess SaO2, the contribution of the arterial blood to the light absorption must be isolated from that of the venous blood, and in pulse oximetry it is achieved by photoplethysmography (PPG)�Cthe measurement of light absorption changes due to the cardiac-induced blood volume changes. The PPG probe consists of a light source emitting light into the tissue and a detector measuring the intensity of light transmitted through the tissue, which decreases during systole because of the systolic increase in the arterial blood volume (Figure 2). Since the PPG pulse represents light absorption in arterial blood, PPG signals at two wavelengths enable the assessment of oxygen saturation in the arterial blood [2]. As will be explained below, there is still a discrepancy between the value of arterial oxygen saturation obtained by pulse oximetry and that obtained by direct measurements in blood extracted from the arteries, and it is therefore customary to designate the former by SpO2 while the latter retains the name SaO2. [4�C6].Figure 1.

The proposed wireless e-nose network system is able to collect remote odor data in real time and conduct further analysis for effective odor management.In environment monitoring using a wireless e-nose network [3,5,6], accurate odor measurement is essential for many applications such as development of odor dispersion models and estimation of odor source location based on the odor data [7]. A wireless e-nose network system is composed of many e-nose nodes that are deployed in a monitoring region. These e-nose nodes are composed of an array of Metal-Oxide Semiconductor (MOS) gas sensors. The output signals from the MOS gas sensors contain not only gas signals, but also noise. The noise results in inaccuracies in analyzing data and estimating the odor strength.

In a previous study, an e-nose consisted of a sensor array and an intelligent analysis system was developed, but the noise reduction of gas sensors was not well investigated [8,9].The Kalman filtering algorithm is a recursive algorithm to solve the state estimation problems of known systems based on certain mathematical models and the observation of noisy measurements. Many modified filtering schemes have been developed to tackle the problems in various applications [10], e.g., a decentralized Kalman filtering algorithm to estimate collaborative information in wireless sensor networks [11], an adaptive Kalman filtering algorithm to reduce the noise for GPS and INS systems [12].In this paper, a wireless e-nose prototype is developed to acquire MOS gas sensor output signals and send them to a remote server.

A modified Kalman filtering technique is developed for improving the sensor sensitivity and precision of odor strength measurement. It can adapt in real time to adjust the measurement Carfilzomib noise variance of the filter parameters. In addition, the optimal parameter of system noise variance is obtained by using the experimental data. Application of Kalman filter theory to the acquired MOS gas sensors data is discussed.2.?Hardware DevelopmentThe block diagram of the proposed e-nose prototype is presented in Figure 1. It is mainly composed of two parts: the odorant gas measurement chamber unit, and the signal processing and wireless communication unit.Figure 1.Block diagram of the e-nose prototype.2.1. Development of the e-nose prototypeThe odorant gas measurement chamber unit is shown in Figure 2.

Based on previous extensive investigation and experiments, four MOS gas sensors (listed in Table 1) were adopted [13]. These four gas sensors can measure most of the major odorant gas compounds found in livestock farm odors. An electrical board is perforated with some holes and the four sensor pedestals are placed circularly; these pedestals have good compatibility, and can easily be replaced by different gas sensors. This electrical board is fixed on a plastic material chamber by using screws and nuts.