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In particular, great influence on measurements is evident for the high-temperature radiation pyrometer in the spectral range of 2-2.7 µm, which is in use in our laboratory as a traceable reference for high-temperature calibrations. Regarding the calibration process, a requirement arose for matching the humidity parameters during the temperature reference transfer to the lower tiers in the chain of traceability. Narrowing of the permitted range of humidity during the calibration, monitoring, and listing of atmospheric parameters in calibration certificates is necessary, for at least this thermometer and possibly for other thermometers as well.Simulation is a useful and common technique to evaluate the performance of networks when the implementation of a real scenario is not available. Specifically for Wireless Body Area Networks (WBAN), it is crucial to perform evaluations in environments as close as possible to the real conditions of use. To achieve that, simulations must include different protocol layers involved in WBAN and models close to reality to create realistic simulation environments for e-health applications. To satisfy these needs, this work presents the BNS framework, a flexible tool for WBAN simulations. The proposal is an extension of the Castalia framework, which includes (1) a new wireless channel model considering real radio-propagation over the human body; (2) an updated implementation of the WBAN MAC protocol in Castalia, with functionalities and requirements in accordance with the IEEE 802.15.6 standard; (3) a new comprehensive and configurable mobility model for simulating intra-WBAN communication; (4) a temperature module based on the Pennes bioheat transfer equation, to model the temperature of a WBAN node based on the activity of the node; and (5) a Healthcare Application Layer that implements data representation and a communication protocol between Personal Health Devices (PHD) following the ISO/IEEE 11073 standard. Three use cases are presented, where WBAN scenarios are simulated and evaluated using the proposed BNS framework. Results show that BNS is a valid and flexible tool to evaluate WBAN solutions through simulation.Embedded systems typically operate in harsh environments, such as where there is external shock, insufficient power, or an obsolete sensor after the replacement cycle. Despite these harsh environments, embedded systems require data integrity for accurate operation. Unintended data changes can cause a serious error in reduced instruction set computer (RISC)-based small embedded systems. For instance, if communication is performed on an edge, where there is insufficient power supply, the peak threshold is not reached, resulting in data transmission failure or incorrect data transmission. To ensure data integrity, we use an error-correcting code (ECC), which can detect and correct errors. The ECC parity bit and data are stored together using additional ECC memory, and the original data are extracted through the ECC decoding process. The process of extracting the original data is executed in the instruction fetch stage, where a bottleneck appears in the RISC-based structure. When the ECC decoding process is execuo 61%. However, it showed an improvement in overall reliability and a 7% increase in speed.A single server GI/M/1 queue with a limited buffer and an energy-saving mechanism based on a single working vacation policy is analyzed. The general independent input stream and exponential service times are considered. When the queue is empty after a service completion epoch, the server lowers the service speed for a random amount of time following an exponential distribution. Packets that arrive while the buffer is saturated are rejected. The analysis is focused on the duration of the time period with no packet losses. A system of equations for the transient time to the first buffer overflow cumulative distribution functions conditioned by the initial state and working mode of the service unit is stated using the idea of an embedded Markov chain and the continuous version of the law of total probability. The explicit representation for the Laplace transform of considered characteristics is found using a linear algebra-based approach. The results are illustrated using numerical examples, and the impact of the key parameters of the model is investigated.In this work, an analytical procedure for the preliminary design of shape memory alloy spring-based actuators is investigated. Two static analytical models are considered and interconnected in the frame of the proposed procedure. The first model, based on the works from An, is able to determine the material properties of the SMA components by means of experimental test data and is able to size the SMA component based on the requirements of the system. The second model, based on a work from Spaggiari, helps to design and size an antagonist spring system that allows one to obtain the geometric characteristics of springs (SMA and bias) and the mechanical characteristics of the entire actuator. The combined use of these models allows one to define and size a complex SMA actuator based on the actuation load requirements. To validate the design procedure, static experimental tests have been performed with the entire SMA actuator.It is shown, theoretically and numerically, that the distributions of the longitudinal energy flow for tightly focused light with circular and linear polarization are the same, and that the spot has circular symmetry. It is also shown that the longitudinal energy flows are equal for optical vortices with unit topological charge and with radial or azimuthal polarization. The focal spot has a minimum diameter (all other characteristics being equal), which is measured based on the intensity of an optical vortex with azimuthal polarization. The diameter of the focal spot calculated from the energy flow for light with circular or linear polarization is slightly larger (by a fraction of a percentage). The magnitude of the diameter based on the intensity plays a role in the interaction of light with matter, and the magnitude of the diameter based on the energy flux affects the resolution in optical microscopy which is crucial in sensorial applications.Vital signs monitoring in physical activity (PA) is of great significance in daily healthcare. Impulse Radio Ultra-WideBand (IR-UWB) radar provides a contactless vital signs detection approach with advantages in range resolution and penetration. Several researches have verified the feasibility of IR-UWB radar monitoring when the target keeps still. However, various body movements are induced by PA, which lead to severe signal distortion and interfere vital signs extraction. To address this challenge, a novel joint chest-abdomen cardiopulmonary signal estimation approach is proposed to detect breath and heartbeat simultaneously using IR-UWB radars. The movements of target chest and abdomen are detected by two IR-UWB radars, respectively. Considering the signal overlapping of vital signs and body motion artifacts, Empirical Wavelet Transform (EWT) is applied on received radar signals to remove clutter and mitigate movement interference. Moreover, improved EWT with frequency segmentation refinement is applied on each radar to decompose vital signals of target chest and abdomen to vital sign-related sub-signals, respectively. After that, based on the thoracoabdominal movement correlation, cross-correlation functions are calculated among chest and abdomen sub-signals to estimate breath and heartbeat. The experiments are conducted under three kinds of PA situations and two general body movements, the results of which indicate the effectiveness and superiority of the proposed approach.Developing various nanosensors with superior performance for accurate and sensitive detection of some physical signals is essential for advances in electronic systems. Zinc oxide (ZnO) is a unique semiconductor material with wide bandgap (3.37 eV) and high exciton binding energy (60 meV) at room temperature. ZnO nanostructures have been investigated extensively for possible use as high-performance sensors, due to their excellent optical, piezoelectric and electrochemical properties, as well as the large surface area. In this review, we primarily introduce the morphology and major synthetic methods of ZnO nanomaterials, with a brief discussion of the advantages and weaknesses of each method. Then, we mainly focus on the recent progress in ZnO nanosensors according to the functional classification, including pressure sensor, gas sensor, photoelectric sensor, biosensor and temperature sensor. We provide a comprehensive analysis of the research status and constraints for the development of ZnO nanosensor in each category. Finally, the challenges and future research directions of nanosensors based on ZnO are prospected and summarized. https://www.selleckchem.com/products/sr4370.html It is of profound significance to research ZnO nanosensors in depth, which will promote the development of artificial intelligence, medical and health, as well as industrial, production.With the increasing amounts of terminal equipment with higher requirements of communication quality in the emerging fifth generation mobile communication network (5G), the energy consumption of 5G base stations (BSs) is increasing significantly, which not only raises the operating expenses of telecom operators but also imposes a burden on the environment. To solve this problem, a two-step energy management method that coordinates 5G macro BSs for 5G networks with user clustering is proposed. The coordination among the communication equipment and the standard equipment in 5G macro BSs is developed to reduce both the energy consumption and the electricity costs. A novel user clustering method is proposed together with Benders decomposition to accelerate the solving process. Simulation results show that the proposed method is computationally efficient and can ensure near-optimal performance, effectively reducing the energy consumption and electricity costs compared with the conventional dispatching scheme.In the important and challenging field of environmental sound classification (ESC), a crucial and even decisive factor is the feature representation ability, which can directly affect the accuracy of classification. Therefore, the classification performance often depends to a large extent on whether the effective representative features can be extracted from the environmental sound. In this paper, we firstly propose a sub-spectrogram segmentation with score level fusion based ESC classification framework, and we adopt the proposed convolutional recurrent neural network (CRNN) for improving the classification accuracy. By evaluating numerous truncation schemes, we numerically figure out the optimal number of sub-spectrograms and the corresponding band ranges, and, on this basis, we propose a joint attention mechanism with temporal and frequency attention mechanisms and use the global attention mechanism when generating the attention map. Finally, the numerical results show that the two frameworks we proposed can achieve 82.1% and 86.4% classification accuracy on the public environmental sound dataset ESC-50, respectively, which is equivalent to more than 13.5% improvement over the traditional baseline scheme.
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