Sensors

Fida, Benish;Bernabucci, Ivan;Bibbo, Daniele;Conforto, Silvia;Schmid, Maurizio

doi: 10.3390/s150923095pmid: 26378544

Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. For monitoring and fitness applications, it is crucial to develop methods able to segment each activity cycle, e.g., a gait cycle, so that the successive classification step may be more accurate. To increase detection accuracy, pre-processing is often used, with a concurrent increase in computational cost. In this paper, the effect of pre-processing operations on the detection and classification of locomotion activities was investigated, to check whether the presence of pre-processing significantly contributes to an increase in accuracy. The pre-processing stages evaluated in this study were inclination correction and de-noising. Level walking, step ascending, descending and running were monitored by using a shank-mounted inertial sensor. Raw and filtered segments, obtained from a modified version of a rule-based gait detection algorithm optimized for sequential processing, were processed to extract time and frequency-based features for physical activity classification through a support vector machine classifier. The proposed method accurately detected >99% gait cycles from raw data and produced >98% accuracy on these segmented gait cycles. Pre-processing did not substantially increase classification accuracy, thus highlighting the possibility of reducing the amount of pre-processing for real-time applications.

Korobiichuk, Igor;Bezvesilna, Olena;Ilchenko, Andriі;Shadura, Valentina;Nowicki, Michał;Szewczyk, Roman

doi: 10.3390/s150922899pmid: 26378535

A thermo-anemometric flowmeter design and the principles of its work are presented in the article. A mathematical model of the temperature field in a stream of biofuel is proposed. This model allows one to determine the fuel consumption with high accuracy. Numerical modeling of the heater heat balance in the fuel flow of a thermo-anemometric flowmeter is conducted and the results are analyzed. Methods for increasing the measurement speed and accuracy of a thermo-anemometric flowmeter are proposed.

Boujday, Souhir;Chapelle, Marc Lamy de la;Srajer, Johannes;Knoll, Wolfgang

doi: 10.3390/s150921239pmid: 26343666

In this short summary we summarize some of the latest developments in vibrational spectroscopic tools applied for the sensing of (small) molecules and biomolecules in a label-free mode of operation. We first introduce various concepts for the enhancement of InfraRed spectroscopic techniques, including the principles of Attenuated Total Reflection InfraRed (ATR-IR), (phase-modulated) InfraRed Reflection Absorption Spectroscopy (IRRAS/PM-IRRAS), and Surface Enhanced Infrared Reflection Absorption Spectroscopy (SEIRAS). Particular attention is put on the use of novel nanostructured substrates that allow for the excitation of propagating and localized surface plasmon modes aimed at operating additional enhancement mechanisms. This is then be complemented by the description of the latest development in Surface- and Tip-Enhanced Raman Spectroscopies, again with an emphasis on the detection of small molecules or bioanalytes.

Yang, Lei;Ren, Yanyun;Hu, Huosheng;Tian, Bo

doi: 10.3390/s150923004pmid: 26378540

In order to deal with the problem of projection occurring in fall detection with two-dimensional (2D) grey or color images, this paper proposed a robust fall detection method based on spatio-temporal context tracking over three-dimensional (3D) depth images that are captured by the Kinect sensor. In the pre-processing procedure, the parameters of the Single-Gauss-Model (SGM) are estimated and the coefficients of the floor plane equation are extracted from the background images. Once human subject appears in the scene, the silhouette is extracted by SGM and the foreground coefficient of ellipses is used to determine the head position. The dense spatio-temporal context (STC) algorithm is then applied to track the head position and the distance from the head to floor plane is calculated in every following frame of the depth image. When the distance is lower than an adaptive threshold, the centroid height of the human will be used as the second judgment criteria to decide whether a fall incident happened. Lastly, four groups of experiments with different falling directions are performed. Experimental results show that the proposed method can detect fall incidents that occurred in different orientations, and they only need a low computation complexity.

Yoo, Wook Jae;Shin, Sang Hun;Lee, Dong Eun;Jang, Kyoung Won;Cho, Seunghyun;Lee, Bongsoo

doi: 10.3390/s150921265pmid: 26343667

We fabricated a small-sized, flexible, and insertable fiber-optic radiation sensor (FORS) that is composed of a sensing probe, a plastic optical fiber (POF), a photomultiplier tube (PMT)-amplifier system, and a multichannel analyzer (MCA) to obtain the energy spectra of radioactive isotopes. As an inorganic scintillator for gamma-ray spectroscopy, a cerium-doped lutetium yttrium orthosilicate (LYSO:Ce) crystal was used and two solid-disc type radioactive isotopes with the same dimensions, cesium-137 (Cs-137) and cobalt-60 (Co-60), were used as gamma-ray emitters. We first determined the length of the LYSO:Ce crystal considering the absorption of charged particle energy and measured the gamma-ray energy spectra using the FORS. The experimental results demonstrated that the proposed FORS can be used to discriminate species of radioactive isotopes by measuring their inherent energy spectra, even when gamma-ray emitters are mixed. The relationship between the measured photon counts of the FORS and the radioactivity of Cs-137 was subsequently obtained. The amount of scintillating light generated from the FORS increased by increasing the radioactivity of Cs-137. Finally, the performance of the fabricated FORS according to the length and diameter of the POF was also evaluated. Based on the results of this study, it is anticipated that a novel FORS can be developed to accurately measure the gamma-ray energy spectrum in inaccessible locations such as narrow areas and holes.

Kang, Wonseok;Yu, Soohwan;Seo, Doochun;Jeong, Jaeheon;Paik, Joonki

doi: 10.3390/s150922826pmid: 26378532

In very high-resolution (VHR) push-broom-type satellite sensor data, both destriping and denoising methods have become chronic problems and attracted major research advances in the remote sensing fields. Since the estimation of the original image from a noisy input is an ill-posed problem, a simple noise removal algorithm cannot preserve the radiometric integrity of satellite data. To solve these problems, we present a novel method to correct VHR data acquired by a push-broom-type sensor by combining wavelet-Fourier and multiscale non-local means (NLM) filters. After the wavelet-Fourier filter separates the stripe noise from the mixed noise in the wavelet low- and selected high-frequency sub-bands, random noise is removed using the multiscale NLM filter in both low- and high-frequency sub-bands without loss of image detail. The performance of the proposed method is compared to various existing methods on a set of push-broom-type sensor data acquired by Korean Multi-Purpose Satellite 3 (KOMPSAT-3) with severe stripe and random noise, and the results of the proposed method show significantly improved enhancement results over existing state-of-the-art methods in terms of both qualitative and quantitative assessments.

Fu, Li;Zhang, Jun;Li, Rui;Cao, Xianbin;Wang, Jinling

doi: 10.3390/s150922854pmid: 26378533

In the 1980s, Global Positioning System (GPS) receiver autonomous integrity monitoring (RAIM) was proposed to provide the integrity of a navigation system by checking the consistency of GPS measurements. However, during the approach and landing phase of a flight path, where there is often low GPS visibility conditions, the performance of the existing RAIM method may not meet the stringent aviation requirements for availability and integrity due to insufficient observations. To solve this problem, a new RAIM method, named vision-aided RAIM (VA-RAIM), is proposed for GPS integrity monitoring in the approach and landing phase. By introducing landmarks as pseudo-satellites, the VA-RAIM enriches the navigation observations to improve the performance of RAIM. In the method, a computer vision system photographs and matches these landmarks to obtain additional measurements for navigation. Nevertheless, the challenging issue is that such additional measurements may suffer from vision errors. To ensure the reliability of the vision measurements, a GPS-based calibration algorithm is presented to reduce the time-invariant part of the vision errors. Then, the calibrated vision measurements are integrated with the GPS observations for integrity monitoring. Simulation results show that the VA-RAIM outperforms the conventional RAIM with a higher level of availability and fault detection rate.

Stratoulias, Dimitris;Balzter, Heiko;Sykioti, Olga;Zlinszky, András;Tóth, Viktor R.

doi: 10.3390/s150922956pmid: 26378538

Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investigations of the capabilities of Sentinel-2 in regard to the spatial and spectral dimensions are needed to assess its potential and the quality of the expected output. This study presents the first simulation of the high spatial resolution (i.e., 10 m and 20 m) bands of Sentinel-2 for lakeshore mapping, based on the satellite’s Spectral Response Function and hyperspectral airborne data collected over Lake Balaton, Hungary in August 2010. A comparison of supervised classifications of the simulated products is presented and the information loss from spectral aggregation and spatial upscaling in the context of lakeshore vegetation classification is discussed. We conclude that Sentinel-2 imagery has a strong potential for monitoring fine-scale habitats, such as reed beds.

Zhang, Haibin;He, Qingbo;Kong, Fanrang

doi: 10.3390/s150921169pmid: 26343662

Stochastic resonance (SR) has been proved to be an effective approach for weak sensor signal detection. This study presents a new weak signal detection method based on a SR in an underdamped system, which consists of a pinning potential model. The model was firstly discovered from magnetic domain wall (DW) in ferromagnetic strips. We analyze the principle of the proposed underdamped pinning SR (UPSR) system, the detailed numerical simulation and system performance. We also propose the strategy of selecting the proper damping factor and other system parameters to match a weak signal, input noise and to generate the highest output signal-to-noise ratio (SNR). Finally, we have verified its effectiveness with both simulated and experimental input signals. Results indicate that the UPSR performs better in weak signal detection than the conventional SR (CSR) with merits of higher output SNR, better anti-noise and frequency response capability. Besides, the system can be designed accurately and efficiently owing to the sensibility of parameters and potential diversity. The features also weaken the limitation of small parameters on SR system.

Chen, Ruizhi;Chu, Tianxing;Liu, Keqiang;Liu, Jingbin;Chen, Yuwei

doi: 10.3390/s150921219pmid: 26343665

This paper introduces a framework for inferring human activities in mobile devices by computing spatial contexts, temporal contexts, spatiotemporal contexts, and user contexts. A spatial context is a significant location that is defined as a geofence, which can be a node associated with a circle, or a polygon; a temporal context contains time-related information that can be e.g., a local time tag, a time difference between geographical locations, or a timespan; a spatiotemporal context is defined as a dwelling length at a particular spatial context; and a user context includes user-related information that can be the user’s mobility contexts, environmental contexts, psychological contexts or social contexts. Using the measurements of the built-in sensors and radio signals in mobile devices, we can snapshot a contextual tuple for every second including aforementioned contexts. Giving a contextual tuple, the framework evaluates the posteriori probability of each candidate activity in real-time using a Naïve Bayes classifier. A large dataset containing 710,436 contextual tuples has been recorded for one week from an experiment carried out at Texas A&M University Corpus Christi with three participants. The test results demonstrate that the multi-context solution significantly outperforms the spatial-context-only solution. A classification accuracy of 61.7% is achieved for the spatial-context-only solution, while 88.8% is achieved for the multi-context solution.