ALBERT

Description: Commercial, industrial, and military aerospace designs are increasingly deploying MEMS micro inertial measurement unit (MIMU) for motion control, automation, and positioning applications, such as the unmanned aerial vehicle (UAV), robot, and smart phone. On the one hand, MIMU has the merit of low cost, small size, low-power consumption, and high shock resistance, but on the other hand, low-cost MIMU is affected by systematic error caused by the instability of the drift, scaling factors, and axes misalignment, which may lead to large errors in the position and attitude’s determination from time to time. That means calibration before use is an effective way to improve the practical precision of MIMU. However, many customers have no precise turntable to calibrate the MIMU before they use it. To address these problems, this paper presents an easy self-calibration method to implement calibration of the MIMU on a common table only with an inclined surface, no precise turntable is needed. The calibration method is based on the following principles. First, the module of the output vector of the orthogonal configured three-axis accelerometers is equal to unit gravity. Second, when IMU rotates to a known gesture with a stable axis, the angles can be calculated through integration. Third, when the accelerometers’ parameters are calculated, it can act as a level datum. Furthermore, the accelerometers on the inclined surface are used to determine the rotating heading datum. Finally, after a series static positions test and rotating test, the parameters can be extracted and estimated. To demonstrate the success and the convenience of the proposed method, comparison experiments with the precision turntable have been made on an ADI’s MIMU. The calibration results show that the accuracy and precision of this method is quite equivalent with the turntable-based calibration, and the scale factors error with an order of magnitude always equal or less than $10^{-5}$ . The observed static and dynamic yaw maximum angular error in a certain period is <0.8°, the pitch maximum angular error is <0.5°, and the roll maximum angular error is <0.3°.

Description: Low-energy technologies in the Internet of Things (IoTs) era are still unable to provide the reliability needed by the industrial world, particularly in terms of the wireless operation that pervasive deployments demand. While the industrial wireless performance has achieved an acceptable degree in communications, it is no easy task to determine an efficient energy-dimensioning of the device in order to meet the application requirements. This is especially true in the face of the uncertainty inherent in energy harvesting. Thus, it is of utmost importance to model and dimension the energy consumption of the IoT applications at the pre-deployment or pre-production stages, especially when considering critical factors, such as reduced cost, life-time, and available energy. This paper presents a comprehensive model for the power consumption of wireless sensor nodes. The model takes a system-level perspective to account for all energy expenditures: communications, acquisition and processing. Furthermore, it is based only on parameters that can empirically be quantified once the platform (i.e., technology) and the application (i.e., operating conditions) are defined. This results in a new framework for studying and analyzing the energy life-cycles in applications, and it is suitable for determining in advance the specific weight of application parameters, as well as for understanding the tolerance margins and tradeoffs in the system.

Description: With the development of Internet of Things (IoT), a large amount of machine-to-machine (M2M) devices produce the data from a huge number of M2M communications. A massive access congestion control scheme in M2M communication is important in a wireless M2M network, where a wireless M2M network is constituted by a large number of wireless M2M devices. In this paper, we developed a spiderweb-based massive access management protocol for M2M wireless networks. The wireless M2M network topology considered, in this paper, is a spiderweb-based topology, which belongs to a regular topology. The spiderweb-based topology is split into a number of sub-spiderweb, while each sub-spiderweb can be seen as a small reservoir. The main function of the small reservoir is to temporarily store the massive access if the congestion problem is occurred due to the massive assess has been suddenly happened from the M2M devices. When the congestion problem is alleviated gradually, the temporarily-stored massive assess can be digested from these small reservoirs. The contribution of the proposed scheme is to reduce the packet loss rate and prolong the wireless M2M network lifetime. In addition, a dynamically adjustment of the different sub-spiderweb size is developed based on the amount of different data traffics. Simulation result finally illustrates that the proposed mechanism significantly reduces the packet loss rate and keeps the maximum network lifetime.

Description: The paper presents the methodology including the deployment of a structure comprising of C-clamp magnetic stripes and a self-calibration scheme using a four-coil design to resolve the issues of the interference and misplacement problems existing in the flexible nonintrusive current sensor tag for the current detection of household two-wire power lines. Experimental results show that the stripe structure can effectively screen out the magnetic flux originating from the nearby power line for the reduction of sensing error caused by the electromagnetic field interference and the calibration scheme containing the correction of horizontal misplacement and the analysis of power cable thickness via the four-coil design can provide the information of sensor location for accurate current sensing. Less than 4% of sensing error indicates that the proposed methodology can indeed facilitate the flexible current sensor tag for practical use.

Description: Rapidly emerging batteryless sensors are creating tremendous opportunities for truly wearable sensors for activity recognition. However, data streams from such sensors are characterized by sparsity and noise, which make activity recognition a challenging task. In this paper, we study the feasibility of passive computational RFID sensors for ambulatory monitoring. In particular, we focus on recognizing transfers out of beds or chairs and walking. Ideally, all these activities need to be monitored by movement sensor alarm systems to alert caregivers to provide supervision during the ambulation of older people in hospitals and nursing homes to prevent a fall. Our novel approach to partition continuous sensor data on natural activity boundaries and to identify transfers out of beds or chairs and walking as transitions between sequences of movements overcomes issues posed by the sparsity and the noise. We demonstrate through in-depth experiments the high performance (F-score > 93%) and the responsiveness of our approach.

Description: This paper proposes an approach to design multiple-input multiple-output radar waveforms that are orthogonal on both the transmitter and receiver. The proposed method jointly utilizes the direct sequence spread spectrum coding and orthogonal frequency division multiplexing (OFDM) chirp signaling techniques. We name it spread spectrum-coded OFDM chirp waveform diversity design. The performance of the designed waveforms is analyzed by examining the ambiguity function and correlation function. The influences of the spread spectrum code choice and the OFDM chirp parameters are also investigated. It is verified that the proposed design scheme can ensure these waveforms stay orthogonal on the receiver and have large time-bandwidth product which is beneficial to separate closely spaced targets.

Description: Studies have presented that the driver vigilance level has serious implication in the causation of road accidents. This paper focuses on integrating both the vehicle-based control behavior and physiological state to predict the driver vigilance index which is evaluated by using a smartwatch. The vehicle control behavior can be observed from the steering wheel movement. Our study utilized the smartwatch motion sensors to study the steering wheel behavior. Meanwhile, physiological state of driver reflects the driver capability of safety alert driving which is estimated by photoplethysmogram (PPG) and respiration signals in this paper. The PPG sensor is integrated in a sport wristband with a Bluetooth low energy module, transmitted the PPG signals to smartwatch in real time. The steering angle is derived by the reading from smartwatch built-in accelerometer and gyroscope sensors. On the other hand, the respiration is derived using the PPG peak baseline method. In order to utterly investigate the sleepiness-induced factors, the time, spectral, and phase space domain features are calculated. Considering the smartwatch processing capability, mutual-information technique is applied to designate the ten most descriptive features. Then, the extracted descriptive features are serve as parameters to a classifier to determine the driver aptitude status. The features are analyzed for their correlation with the subjective Koralinska sleepiness scale and through recorded video observations. The experimental results reveal that our system is capable of estimating driver hypervigilance at average of 96.5% accuracy rate by evaluating on both driving behavior and driver physiological state, provided a novel and low-cost implementation.

Description: A cavitating two-phase flow of water in a pipe with area shrinkage was experimentally investigated, acquiring at high sampling rate pressure signals and images of the cavitating flow field. The time series of the pressure fluctuations was analyzed in terms of power spectral density and related to the cavitation regimes. Furthermore, the fluctuations of the pressure measurements were also decomposed using the wavelet transform to analyze the frequency distribution of the signals energy with respect to the flow behavior. The energy content at each frequency band of the acquire signals is well related to cavitation flow-field behavior. Moreover, the artificial neural network and the least squares support vector machine (LS-SVM) were implemented to identify the cavitation regime, using, as inputs, the power spectral density distributions of the pressure fluctuations, and some features of the decomposed signals, as the wavelet energy for each decomposition level and wavelet entropy. Results indicate the most accurate model to be used in the cavitation regime identification, underlining the enhanced capability of LS-SVM trained with the input data set based on the wavelet decomposition features.

Description: In this paper, we present a novel silicon-on-insulator (SOI) complementary metal–oxide–semiconductor (CMOS) microelectromechanical-system thermal wall shear stress sensor based on a tungsten hot-wire and a single thermopile. Devices were fabricated using a commercial 1- $mu text{m}$ SOI-CMOS process followed by a deep reactive ion etching back-etch step to release a silicon dioxide membrane, which mechanically supports and thermally isolates heating and sensing elements. The sensors show an electrothermal transduction efficiency of $50~mu text{W}$ /°C, and a very small zero flow offset. Calibration for wall shear stress measurement in air in the range of 0–0.48 Pa was performed using a suction type, 2-D flow wind tunnel. The sensors were found to be extremely sensitive, up to 4 V/Pa for low wall shear stress values. Furthermore, we demonstrate the superior signal-to-noise ratio (up to five times higher) of a single thermopile readout configuration compared with a double thermopile readout configuration (embedded for comparison purposes within the same device). Finally, we verify that the output of the sensor is proportional to the cube root of the wall shear stress and we propose an accurate semiempirical formula for its modeling.

Description: We propose an optical fiber hydrostatic pressure sensor based on micro-cavities generated by the fiber fuse effect. The presented sensor is manufactured through the recycling of optical fiber destroyed by the fiber fuse effect, being, therefore, a cost-effective solution, when compared with other similar micro-cavity-based solutions. The developed sensor was characterized for pressures up to 20 kPa, showing a linear sensitivity coefficient of $0.47pm 0.03~{rm nm}cdot {rm kPa}^{-1}$ , for pressure values below 8 kPa. Furthermore, we propose a new theoretical model to describe the behavior of the microcavities embedded in optical fibers. This allows us to solve the discrepancies, already identified by other authors, between the experimental results and the ones attained with the flat mirrors Fabry-Perot model. By this way, we were able to describe the sensor response, within the full dynamic range.