The objective of this article was to compare the performance of two vegetation indices (MODIS EVI (optical) and AMSR-E/ and TMI/TRMM LPRM VOD (microwave)) using an offline Bayesian change-point algorithm to monitor vegetation dynamics (retrospective analysis). We tested this model by simulating 8-day EVI and VOD time series with varying amounts of seasonality, noise, length of the time series and by adding abrupt changes with different magnitudes. This model was applied over real time series (optical and microwave) over a dry forest area in Argentina, Dry Chaco Forest (DCF), where deforestation is common. A comparison with common model used over this region were made (visual inspection). The results compared favourably with Redaf dataset, based on Landsat images. These results show the potential to combine optical and passive microwave indices to identify disturb event. Furthermore, the results obtained in this manuscript are relevant for the DCF region, since provide a fast and alternative model to the traditional visual analysis made by the national forest service and Redaf.

The propagation characteristics of the fundamental symmetric Lamb mode S₀ along thin composite plates based on amorphous SiC and piezoelectric c-ZnO layers was studied, aimed at the design of a high frequency electroacoustic device suitable to work in liquid environment. The investigation of the acoustic field profile across the plate thickness revealed that, up to a a-SiC thickness-to-wavelength ratio h/λ=0.1 the propagating modes have polarization predominantly oriented along the propagation direction, and hence suitable for operation in liquid environment. The presence of the ZnO layer alters the acoustic field profile that is no more symmetric about the mid-plane of the plate but there is a ZnO limited thickness range corresponding to quite unperturbed field profile of the propagating acoustic mode at the bare SiC layer side. The phase velocity and coupling coefficient K² of the S₀ mode were studied with respect to the SiC and ZnO  layers thickness and the electrical boundary conditions, and a K² =7%  was achieved. The thin a-SiC/ZnO acoustic waveguide theoretically investigated here can be fabricated by using conventional thin film deposition techniques, such as rf sputtering, and bulk micromachining of  the Si substrate. The fabrication procedure of this acoustic device offers the advantage of providing the monolithic  integration of the device with the signal processing electronics. The theoretical investigation suggests that a-SiC/ZnO materials combination is a very promising substrate material suitable for developing high-frequency, IC compatible, very simple electroacoustic devices with enhanced K² and suitable for working in liquid environment.

The propagation of Love modes along ZnO/glass-based structures has been modeled and analysed aimed at the design of a sensor able to operate in liquid environment. The Love modes propagation was modeled by numerically solving the system of coupled electro-mechanical field equations and Navier–Stokes equations. The ZnO layer was considered isotropic but its elastic constants were considered numerically equal to the stiffened elastic constants of the 30° tilted piezoelectric counterpart. The sensor velocity and attenuation sensitivities to the changes of liquid viscosity and mass loading were calculated for different ZnO layer thicknesses and  the peak sensitivity was achieved at the ZnO thickness to/wavelength ratio h/λ=0.05. In order to further enhance the sensor sensitivities, the PMMA/ZnO/glass structure was investigated. The phase and group velocities and the attenuation of the acoustic wave propagating along the PMMA /30° tilted c-axis ZnO/glass structure contacting a viscous non-conductive liquid were calculated for different PMMA and ZnO guiding layer thicknesses, added mass thicknesses,  and liquid viscosity and density. The three sensor responses (the wave phase and group velocity, and attenuation changes) were calculated for different environmental parameters and related to the sensor velocity and attenuation sensitivities. The resulted sensitivities to liquid viscosity and added mass were optimized by adjusting the ZnO and PMMA guiding layer thickness corresponding to a sensitivity peak. The present analysis is meaningful for the manufactures and applications of the PMMA-ZnO-glass structure Love wave sensors for the detection of liquids properties, such as viscosity, density and mass anchored to the sensor surface.

This paper deals with all required parts to set up the full variable valve train (FVVT) system to be used in intake and exhaust valves of combustion engines from the electrical/electronic point of view. This includes the displacement and pressure sensors that are needed to conduct research on the fully variable valve train while in operation and the electronic auxiliaries such as the amplifier for the piezo actuator, the dSPACE control system and the used function generator. In particular, the use of displacement transducers for all relevant pistons and stems is crucial for the FVVT system control. As only the piezo actuator is equipped with a displacement sensor, the remaining sensors still need to be chosen. Within the previous FVVT system two different displacement sensors have been used on the engine valve stem. Three displacement sensors and one pressure sensor are implemented into the mechanical core of the system (consisting of lever transmission, control unit, servo valve and engine valve body), which is driven by the piezo actuator and supplied with hydraulic pressure by a pump, that requires a 230 V power supply and a tank, which the hydraulic fluid can return to. While the pressure sensor monitors the hydraulic pressure within the lever transmission, displacement sensors need to be chosen to monitor the motion of the 

1. control piston in the control unit 
2. servo piston within the servo valve
3. engine valves. 

Measured results are presented.

Autonomous service robots are increasingly being used for cleaning, delivering stuff, patrolling, and other tasks like inspection. These robots often use the same passages which are used by people for navigation to specific areas. Robots are equipped with visual sensors, laser or sonar based range estimation sensors to avoid collision with obstacles, people, and other moving robots. However, these sensors have a limited range and are often installed at a lower height (mostly near the robot base) which limits the detection of far-off obstacles. In addition, these sensors are positioned to see forward, and robot is often 'blind' about objects (ex. people and robots) moving behind the robot which increases the chances of collision. We propose to use a network of external cameras fixed on the ceiling (ex. surveillance cameras) to guide the robots by informing about moving obstacles from behind and far-off regions. This enables the robot to have a 'birds-eye view' of the navigation space which enables it to take decisions in real-time to avoid the obstacles efficiently. The camera sensor network is also able to notify the robots about moving obstacles around blind-turns. A mutex based resource sharing scheme in camera sensor network is proposed which allows multiple robots to intelligently share narrow passages through which only one of the robots/person can pass at a given time. Experimental results in simulation and real scenarios show that the proposed method is effective in robot navigation in crowded and narrow passages.

Today sensor data processing and information mining become more and more complex concerning the amount of sensor data to be processed, the data dimension, the data quality, and the relationship between derived information and input data. This is the case especially in large-scale sensing and measuring processes embedded in Cloud environments. Measuring uncertainties, calibration errors, and unreliability of sensors have a significant impact on the derivation quality of suitable information. In the technical and industrial context the raising complexity and distribution of data processing is a special issue. Commonly, information is derived from raw input data by using some kind of mathematical model and functions, but often being incomplete or unknown. If reasoning of statements is primarily desired, Machine Learning can be an alternative. Traditionally, sensor data is acquired and delivered to and processed by a central processing unit. In this paper, the deployment of distributed Machine Learning using mobile Agents forming self-organizing and self-adaptive systems (self-X) is discussed and posing the benefit for the enhancement of the sensor and data processing in technical and industrial systems. This also addresses the quality of the computed statements, e.g., an accurate  prediction  of run-time parameters like  mechanical loads or health conditions, the efficiency, and the reliability in the presence of partial system failures.

Load-bearing engineering structures typically have a static shape fixed during design based on expected usage and associated load cases. But neither can all possible loading situations be foreseen, nor could this large set of conditions be reflected in a practical design methodology - and even if either or even both was possible, the result could only be the best compromise and thus deviate significantly from the optimum solution for any specific load case.
A structure that could change its local properties in service based on the identified loading situation could  potentially raise additional weight saving potentials and thus support lightweight design, and in consequence, sustainability.
Property change of materials has been discussed in the past, e.g, in terms of stiffness changes etc. The present paper provides and overview of such approaches. Materials of this kind would exhibit a cellular architecture consisting of a large number of active cells with sensing and actuation capabilities as basis for local change. As additional part of these active cells, suitable control mechanisms both in terms of algorithms and hardware units are necessary. One major issue existing with a fine-grained active smart cellular structure is the correlated control of each actuator and the informational organization. Among the requirements for the control system there are real-time capabilities and high levels of robustness.
As a control mechanism behind property adaptation, a two-stage approach combining mobile & reactive Multi-agent Systems (MAS) and Machine Learning is foreseen. MAS are used to analyze the loading situation based on sensor information - preferably, highly localized strain data - and negotiate a matching redistribution of, e.g., local stiffness values according to some higher-level aim like minimizing total elastic strain energy or maximum stress levels in the system. The machine learning approach in contrast would recognise loading situations that have already been encountered in the past and on this basis avoid the MAS approach by directly proposing the solution found in the preceding case.  
To achieve these aims, the system should feature self-organization and self-adaptivity in terms of computational units. Planning of the agents (i.e., planning of control actions) should base on Distributed agent-based Machine Learning (DML). In this work, a hybrid learning approach is used with prediction of already known load situations (i.e., supervised trained learning) and reinforcement learning to improve the material adaptation by minimizing selected target parameters. This is performed by the agents by adapting their action planning based on the learning results. One major feature of the DML is the deployment of a collection of spatially distributed learner agents, each learning a local model, which are finally fusioned to a global learner model via negotiation, following a fine-grained Divide-and-Conquer approach.
The present study proposes a virtual evaluation system to analyze potential benefits and develop associated algorithms. This proof of concept should be performed by combining FEM and MAS simulation. The FEM simulation is also used for off-line training of the MAS prior to deployment in a real structure. The classification models learned this way are a starting point and can be updated at run-time by using incremental learning techniques.
Besides providing outlines of the system evaluation, the study will discuss further possible benefits of a system of this kind, including e.g. the possibility of isolating internal damage and compensating its effect on structural performance.

A novel and highly sensitive non-enzymatic glucose biosensor was developed by nucleating colloidal silver nanoparticles (Ag NPs) on MoS₂. The facile fabrication method, high reproducibility (97.5%) and stability indicates a promising capability for large-scale manufacturing. Additionally, the excellent sensitivity (9044.6 μA mM^-1 cm^-2), low detection limit (0.03 μM), appropriate linear range of 0.1-1000 μM, and high selectivity, suggests that this biosensor has a great potential to be applied for noninvasive glucose detection in human body fluids, such as sweat and saliva.

SPR technique possesses rich potential possibilities for investigation of different aspects of virus-specific agent interaction and modification of structure of viruses, induced by external factors. Аccording to World Health Organization, viruses of Herpesviridae family infect 90% of the Earth’s population. Herpetic infection is urgent for several spheres of medicine: infectology, infectious neurology, transplanthology, haemotology. It is perspective today using of biosensor technologies for developing of diagnostic systems.

The aim of this work is develop and characteristics of biosensor chips for detection specific antibodies to herpes simplex virus and Epstein-Barr virus in patients’ blood sera. The study was performed using the device "Plasmon-6", which is a computer-controlled optoelectronic spectrometer, which uses the SPR phenomenon in the optical configuration Kretschmann. It is developed at the Institute of Semiconductor Physics NAS of Ukraine. The advantage of this device is a compact design;  full record of kinetic dependence; the minimum of one measurement: 0.2 sec (mode slope); measurement in the gas or liquid; additional analog channel. We used additional channel as a control one. This allowed neutralize influences of environmental (temperature, humidity and another) at its operation.

As antigens used purified proteins of viruses derived from cell cultures. The selection of sera was carried out using  test kits "HSV-1 IgG ELISA" and «EBV VCA IgG ELISA» (GenWay, USA). Immobilization of viral proteins on sensor surface was performed using 0.2% solution of Dextran 17 000 (Sigma, USA). It was found direct dependence between amount of immobilized antigen and SPR response. The immobilization 8x10-5 mg/mm2 of viral proteins on the surface of the chip was optimal for detection of antibodies. About 200 samples positive and negative blood sera of patients were tested  who were previously tested by ELISA and created combined pools with varying degrees load of antibodies to studied virus. The limits of positive and negative response for SPR analysis was determined by using panel of negative blood sera of donors. SPR data were agreed with ELISA results in 84% of samples. The reproducibility of results varied between 85 - 95%.

Thus, in this study biosensor chip for detection of specific antibodies to HSV-1 and EBV was successfully developed for express diagnostic of these pathogens.

The determination of the pressures affecting the marine works of a vertical nature is far from being completely understood. The prototype instrumentation and testing of large-scale channels exhibit useful information to define their behaviour at probabilistic level, but not the above methods or existing methods of calculation are capable of responding to complex phenomenon that occurs in wave reflection on the vertical dikes. The results offered are based on very simplified principles and calibrated in accordance with a scale trials, leading to overestimate the works, with the over cost that this entails. In this work we have developed a measurement system that allows a study of the laws of pressures acting on vertical breakwaters, and optimize the design and sizing of the same. This system has been placed in the dock of Botafoc (Ibiza - Spain) and the measured data has led to new results and very revealing as to the form of the distributions of the thrusts, the influence of the wave period on the same, and importance of flows induced by waves through the foundation stools.