Sensors2015, 15(3), 5020-5031; doi:10.3390/s150305020 (registering DOI) - published 27 February 2015 Show/Hide Abstract
Abstract: A wireless temperature sensor node composed of a piezoelectric wind energy harvester, a temperature sensor, a microcontroller, a power management circuit and a wireless transmitting module was developed. The wind-induced vibration energy harvester with a cuboid chamber of 62 mm × 19.6 mm × 10 mm converts ambient wind energy into electrical energy to power the sensor node. A TMP102 temperature sensor and the MSP430 microcontroller are used to measure the temperature. The power management module consists of LTC3588-1 and LT3009 units. The measured temperature is transmitted by the nRF24l01 transceiver. Experimental results show that the critical wind speed of the harvester was about 5.4 m/s and the output power of the harvester was about 1.59 mW for the electrical load of 20 kΩ at wind speed of 11.2 m/s, which was sufficient to power the wireless sensor node to measure and transmit the temperature every 13 s. When the wind speed increased from 6 m/s to 11.5 m/s, the self-powered wireless sensor node worked normally.
Sensors2015, 15(3), 4996-5019; doi:10.3390/s150304996 (registering DOI) - published 27 February 2015 Show/Hide Abstract
Abstract: Wireless Sensor Networks constitute pervasive and distributed computing systems and are potentially one of the most important technologies of this century. They have been specifically identified as a good candidate to become an integral part of the protection of critical infrastructures. In this paper we focus on railway infrastructure protection and we present the details of a sensor platform designed to be integrated into a slab track system in order to carry out both installation and maintenance monitoring activities. In the installation phase, the platform helps operators to install the slab tracks in the right position. In the maintenance phase, the platform collects information about the structural health and behavior of the infrastructure when a train travels along it and relays the readings to a base station. The base station uses trains as data mules to upload the information to the internet. The use of a train as a data mule is especially suitable for collecting information from remote or inaccessible places which do not have a direct connection to the internet and require less network infrastructure. The overall aim of the system is to deploy a permanent economically viable monitoring system to improve the safety of railway infrastructures.
Sensors2015, 15(3), 4975-4995; doi:10.3390/s150304975 (registering DOI) - published 27 February 2015 Show/Hide Abstract
Abstract: The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data. The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies.
Sensors2015, 15(3), 4958-4974; doi:10.3390/s150304958 (registering DOI) - published 27 February 2015 Show/Hide Abstract
Abstract: Both optical tweezers and acoustic tweezers have been demonstrated for trapping small particles in diverse biomedical applications. Compared to the optical tweezers, acoustic tweezers have deeper penetration, lower intensity, and are more useful in light opaque media. These advantages enable the potential utility of acoustic tweezers in biological science. Since the first demonstration of acoustic tweezers, various applications have required the trapping of not only one, but more particles simultaneously in both the axial and lateral direction. In this research, a method is proposed to create multiple trapping patterns, to prove the feasibility of trapping micro-particles. It has potential ability to electronically control the location and movement of the particles in real-time. A multiple-focus acoustic field can be generated by controlling the excitation of the transducer elements. The pressure and intensity of the field are obtained by modeling phased array transducer. Moreover, scattering force and gradient force at various positions are also evaluated to analyze their relative components to the effect of the acoustic tweezers. Besides, the axial and lateral radiation force and the trapping trajectory are computed based on ray acoustic approach. The results obtained demonstrate that the acoustic tweezers are capable of multiple trapping in both the axial and lateral directions.
Sensors2015, 15(3), 4947-4957; doi:10.3390/s150304947 (registering DOI) - published 27 February 2015 Show/Hide Abstract
Abstract: In association with the widespread use of prostate specific antigen (PSA) screening, the numbers of men identified with early-stage prostate cancer (PCa) are increasing in the developed countries, including Japan. However, the accurate localization of PCa lesions in diagnostic imaging is still difficult because PCa has a tendency to be multifocal in the prostate gland. Contrast-enhanced ultrasound (CEUS) improves the detection of PCa by visualizing cancerous lesions in order to target a needle biopsy. CEUS has the potential to enable not only accurate diagnoses but also novel treatments such as focal therapy. The combination of CEUS and other modalities is expected to improve the diagnosis of PCa and its treatment.
Sensors2015, 15(3), 4925-4946; doi:10.3390/s150304925 (registering DOI) - published 27 February 2015 Show/Hide Abstract
Abstract: We report on a sensor data fusion algorithm via an extended Kalman filter for estimating the spatial motion of a bipedal robot. Through fusing the sensory information from joint encoders, a 6-axis inertial measurement unit and a 2-axis inclinometer, the robot’s body state at a specific fixed position can be yielded. This position is also equal to the CoM when the robot is in the standing posture suggested by the detailed CAD model of the robot. In addition, this body state is further utilized to provide sensory information for feedback control on a bipedal robot with walking gait. The overall control strategy includes the proposed body state estimator as well as the damping controller, which regulates the body position state of the robot in real-time based on instant and historical position tracking errors. Moreover, a posture corrector for reducing unwanted torque during motion is addressed. The body state estimator and the feedback control structure are implemented in a child-size bipedal robot and the performance is experimentally evaluated.