The possibility of using mobile devices, such as smartphones, for locating a person indoor is becoming more attractive for many applications. Among them are health care and safety services, commercial and emergency applications. One of the approaches to find the smartphone position is known as Pedestrian Dead Reckoning (PDR). PDR relies on the smartphone low-cost sensors, such as accelerometers, gyroscopes, barometer and magnetometers. An appropriate calibration phase to find the step length algorithm gains is required before PDR can be applied. These gains are very sensitive to the user and smartphone mode. In this research, we employ machine learning classifications algorithms in order to recognize and classify the pedestrian and smartphone modes. A methodology of training on a single user and testing on multiple users is proposed and experimentally evaluated. Results show successes in classifying the user and smart phone modes.

The adverse impacts of natural disasters on lives and livelihoods, as well as regional and local economies, are increasingly evident, and losses to both tangible and intangible cultural heritage due to these disasters pay an important role in the total amount. In fact, damages to sites, structures and artifacts of cultural and historical value, as well as impacts to cultural tourism and the financial resources produce a strong competitive disadvantage to local communities. Emergency decision making, based on awareness of the suffered damages, can have a significant impact in the attempts of improving resilience of the strategic elements; however, this process typically requires a fast overview on large territories.

In this work, we propose a novel framework for obtaining an agile solution to quickly collect and analyze picture galleries and information provided by both internal staff and citizens through commercial mobile devices. This solution virtually generates a network of information sources during emergency time (e.g. a seismic sequence), and allows to produce a situation map in GIS environment and hence support the health status analysis of cultural heritage over time. The paper presents the prototype system composed of: 1) a smartphone application for the acquisition of new information and the examination of existing one; 2) a web-service for exchanging data with databases and 3) a local service that makes use of a proper piece of software for obtaining a 3D reconstruction from new picture galleries. The proposed system results in a scalable, exportable and modular tool useful during the emergency and for preserving memories of local communities.

Wheeled mobile robots (WMRs) are very interested for different applications from in-house activities in assisting elderly people and patients to space exploration. While the design concept and the application of the WMRs determine the shape, dimension, and specifications of the prototype robot, the positional errors occur during the operation of the WMR. In order to reduce the effect of positional errors, the motion of WMRs is corrected using calibration techniques. Calibration is defined as a set of operations that establishes, under specified conditions, the relationship between the values of quantities indicated by a measuring instrument and the corresponding values realized by standards. Example of calibration techniques employed to correct the WMRs movement are odometry, 3D camera error detection, active beacons, gyroscope and magnetic compasses. Each technique requires a set of sensory systems to measure the position, orientation, velocity, and/or acceleration of the robot. Odometry provides better short-term accuracy than other methods that allows very high sampling rates with lower costs. In addition, in odometry approach, fewer landmarks are needed to localize the WMR over its trajectory. This paper explains how the odometry technique was employed to improve motions of this robotic platform, and then reports technical results of an attempt to reduce positioning errors of a small WMR with a two-wheeled structure. The technique and instrumented setup could be used to alleviate the errors originating from imperfections in design, prototype, and assembly.

Urban green space, discovered by optical remote sensors, is the area covered by terrestrial vegetation in urban areas, and is considered an important factor in urban sustainability. Two sensors ALOS/VNIR and Landsat/OLI&TIR were used in this study to determine green space by Maximum Likelihood Classification method. The investigated area was Nha Trang city, located in the central Vietnam. This was found that the impervious surfaces were rapidly increased leading to significantly reduce urban green space within 10 years from 2007-2017. In urban areas, the green index was very low compared to the TCXDVN 9257: 2012. Based on the Markov chain, it is projected that over the next 10 years, the total vegetation cover of the city will continue to decline compared to that of today. This is likely to lead to increase catastrophe and environmental risks, especially floods and erosion in the coastal city of Nha Trang. The process could be very useful in mapping urban green space as indicator serving city sustainable development.

Body temperature is an important parameter to measure in a number of fields such as medicine and sport. In medicine temperature changes can indicate underlying pathologies such as wound infections, while in sport temperature can be associated to a change in performance. In both cases a wearable temperature monitoring solution is preferable. In earlier work a temperature sensing yarn has been developed and characterised. The yarns were constructed by embedding an off-the-shelf thermistor into a polymer resin micro-pod and then into the fibres of a yarn. This process created a temperature sensing yarn that was conformal, drapeable, mechanically resilient, and washable. This work builds on this early study with the purposes of identifying the steady state error bought about on the temperature measurements as a result of the polymer resin and yarn fibres. Here a wider range of temperatures than previously explored were investigated. Additionally two types of polymer resin with different thermal properties have been tested, with varying thicknesses, for the encapsulation of the thermistor. This provides useful additional information for optimising the temperature sensing yarn design.

My talk is divided into three parts:

First, I will give a short introduction to the Institute of Sensor and Actuator Systems, our infrastructure and our expertise in the field of micro- and nanomachined devices and systems.

In the second part of my talk, piezoelectric thin films are introduced for MEMS. I will highlight latest results on the electrical, mechanical and piezoelectrical characterization of sputter-deposited aluminium nitride (AlN) including the impact of sputter parameters, film thickness and substrate pre-conditioning. To enhance the moderate piezoelectric coefficients of pure AlN thin films doping with scandium is done leading to an increase of this material parameter up to factor of 4. In a next step, these films are implemented into the fabrication process of cantilever-type MEMS devices. In combination with a tailored electrode design resonators are realized having Q factors up to about 300 in the frequency range of 1-2 MHz in liquids, thus enabling the precise determination of the viscosity and density of fluids. An additional field of application are vibrational energy harvesters, where the benefit of ScAlN compared to AlN is demonstrated.

In the third part of the talk energy harvesting at aircrafts is introduced. Due to the ambient conditions during operation a thermoelectric generator is developed providing enough electrical energy to power wireless sensor nodes for structural health monitoring purposes. The basic concept of the energy harvester as well as first results from flight tests are presented.

This paper presents an alignment method to improve the projection lithography accuracy for linear scale, which is one main component and measurement benchmark of grating linear displacement sensor. The adjacent pixel gray square variance method of CCD image is used to find the best position of the focal length of the motherboard and then realize the alignment on focal plane. Two image positions in the focal plane from the CCD are compared the traits overlap through the image splicing principle, and to establish the correction of four typical errors basis on the whole grating errors. At the same time, using the rotation error of the mask to summarize Grayscale Variation Function of CCD Image, and threshold functions are used to express the factors including wave crests of the amplitude, period error and phase error, which govern the rotation accuracy and weight alignment accuracy expression of the four error factors is established. In the experiment, it is finally corrected the slope of the mask and be adjusted the same direction of the slide plate with the help of dual-frequency laser interferometer, the influence of alignment error on lithography accuracy was discussed and verified in the static case and the CCD maximum resolution pixel corresponds to 0.1μm, the accuracy of scale is 0.79μm in only 200mm measurement range.

The U.S. Environmental Protection Agency (EPA) and Desarrollo Integral del Sur, Inc (DISUR), a Puerto Rico-based community action group, collaborated to determine the efficacy of citizen science involving the use of low cost air quality sensors. The EPA developed a unique low cost AC powered multi-pollutant Citizen Science Air Monitor (CSAM) that was provided to the community group along with the training/tools needed for its operation. The citizens self-organized a community effort to conduct approximately five months of intensive air quality monitoring in an area of Puerto Rico (Tallaboa-Encarnación, Peñuelas) having little historical data on spatial variability (Ponce). Real-time measurements of the particulate matter size fraction 2.5 micron (PM_2.5), nitrogen dioxide (NO₂), total volatile organic compounds (tVOCs), and meteorological parameters (wind speed, wind directions, temperature, relative humidity) were obtained. The study provided the Peñuelas and surrounding communities an in-depth investigation of local air quality and opportunities for citizen scientists to gain extensive experience in the use of emerging sensor technologies. The collaboration also provided the EPA an opportunity to evaluate low cost sensor performance under harsh environmental conditions (high relative humidity in a coastal environment). We present the approach and preliminary environmental findings of the EPA’s efforts the deploy a low cost multi-pollutant sensor pod associated with a citizen science research study. 

In a recent year, the most emerging and growing field of research and development is “Internet of Things” (IoT). It is due to advancement in wireless sensor network (WSN) which operate in the unlicensed industrial, scientific and medical (ISM) spectrum band. However, many wireless technologies operate in the same unlicensed spectrum, makes it overcrowded and hence resulted in spectrum scarcity among those bands, the performance of WSN will degrade as their popularity increases. According to FCC report, most of the licensed spectrum is underutilized, sharing of underutilized licensed spectrum among unlicensed devices is a promising solution to the spectrum scarcity issue. Cognitive Radio (CR) is one of the capable technology that allows sensor nodes (as a Secondary Users (SUs)) to detect and use the underutilized licensed spectrum temporarily when Primary Users (PUs) not using it. With recent advances in Cognitive Radio (CR) technology, it is possible to apply the Dynamic Spectrum Access (DSA) model in WSNs to get access to underutilized licensed spectrum, possibly with better propagation characteristics, but as the existing protocols and algorithms developed for CRNs and WSNs are not directly applicable to CR-based WSNs and required new protocols. In this paper, we present a survey on the novel design of CR-based MAC, identify the main advantages and challenges of using CR technology, and compare the different method of improving energy efficiency. We believe that CR-WSN is the next-generation WSN. In this paper, we also discussed the open issues to motivate new research interest in this field.