Bottom Sensors Research Articles

A Perceptual and Interactive Integration Strategy Toward Telemedicine Healthcare Based on Electroluminescent Display and Triboelectric Sensing 3D Stacked Device

AbstractThe conventional medical assistive strategy for wearable devices involves sensing physiological signals and subsequent analysis while it lacks the capability for visual interaction and often necessitates the use of additional bulky devices for display. Herein, a perceptual and interaction strategy based on electroluminescent and triboelectric sensing 3D stacked wearable device (ETD) is proposed to address current issues. The ETD utilizes a bottom triboelectric sensor for tactile physiological information, which is processed and analyzed, ultimately triggering the selective illumination of the top electroluminescent array through external power circuitry. The ETD possesses the capability to generate a 100 V with 1.5 cm2 and stable visual interaction ability at 30 V drive voltage. Based on outstanding features of the ETD, two medical assistance scenarios are explored. I) The ETD captures physiological signals, such as pulse rate, respiratory rate, and special joint flexion, and in situ displays the corresponding physiological information, effectively assisting the patients/elders in self‐monitoring. II) A bidirectional interactive telemedicine assistive system utilizing ETDs is implemented, which not only gathers real‐time activity status from patients/elders but also realizes that remote physician assesses the safety status and the assessment results are wirelessly back‐communicated to the ETD, enabling real‐time interaction of the physician advice.

Design and fabrication of a sandwich detector for material discrimination and contrast cancellation in dual-energy based x-ray imaging

Dual-energy imaging represents a versatile and evolving technology with wide-ranging applications in medicine and beyond. Recent technological developments increased the potential for improved diagnostic accuracy and expanded imaging capabilities across various fields. The purpose of this work is to design and develop an energy-integrating multilayer detector, known as a sandwich detector, aimed at single-shot dual-energy imaging tasks such as material discrimination and contrast cancellation. The sandwich detector uses two complementary metal oxide semiconductor advanced pixel sensors of 50 μm pixel size. The top and bottom sensors detect low-energy (LE) and high-energy (HE) photons, with sensors coupled with 250 and 600 μm scintillators, respectively. For better spectral separation between layers without excessively affecting the detected statistic in the bottom layer, the insertion of a 0.25-mm Cu filter between the layers was found to be the optimal choice, from among the tested 0-, 0.25-, and 0.5-mm filter options. The thickness selection for scintillator and intermediate Cu filter was carried out through a dual-energy simulation model. The experiments confirmed the model's reliability in selecting the optimal thicknesses of the intermediate Cu filter, thereby providing reassurance also on the choice of the top scintillator.

Smart Blind Stick Using ESP32

Abstract: The Smart Blind Stick integrates ESP32 microcontroller, two ultrasonic sensors (middle and bottom), and a soil moisture sensor to enhance safety for visually impaired individuals. The middle ultrasonic sensor detects head-level obstacles, triggering a vibration motor for tactile feedback. The bottom ultrasonic sensor identifies potholes and manholes, activating a buzzer for audible alerts. A soil moisture sensor warns of wet surfaces through a speaker with IS 1820 IC, aiding users in avoiding hazards. This compact system enables proactive navigation, empowering users with improved situational awareness and confidence.

Development and characterization of a highly sensitive magnetic electronic skin for intelligent manipulators

A magnetic electronic skin (e-skin) with high sensitivity, flexibility, and wide force measurement range was designed. The e-skin includes a top magnetic film layer, a middle polydimethylsiloxane (PDMS) layer and a bottom tunneling magnetoresistive sensor layer. The magnetic film layer is composed of neodymium-iron-boron particles (Nd2Fe14B) and PDMS. A test platform was established. The test results showed that the e-skin could measure forces in the range of 0.05–20 N, with a static sensitivity of 287 mV/N within 0–1 N, followed by a gradual decrease in sensitivity with an increase in static force. The resolution of the electronic skin was 0.05 N, and the change in sensitivity under dynamic force was less than 3.17 %. The response time and recovery time were 41 ms and 40 ms, respectively. This study provides a foundation for further research and development of advanced electronic skins for use in a variety of flexible electronic applications.

The dependence of wave height probability distributions on physical parameters from measurements near Sakhalin Island

The data of long-term surface waves measurements with bottom sensors near Sakhalin Island were used to build instrumental probability distributions for exceedance of wave heights. Waves with heights exceeding the significant wave height by more than three times were recorded. Specific features of the observations conducted during the periods of open and ice-covered sea surfaces are discussed. The subsets of statistically homogeneous data are arranged through selection considering the natural physical dimensionless parameters of the task, that control the effects of finite depth and nonlinearity (namely, the wave steepness, the wave amplitude to water depth ratio, the Ursell parameter). The manifestation of these and composite parameters in theoretical probability distributions of wave heights is discussed. The effects of nonlinearity and the measurement point depth on probability distributions are estimated with a focus on abnormally high waves. In particular, it is shown that for the place of registration an increase in the wave amplitude to water depth ratio parameter leads to a decrease in the probability of abnormally high waves. This behavior is consistent with the Glukhovsky theoretical distribution. The waves characterized by relatively large dimensionless depth parameter exhibit a higher probability of substantial exceedance of the significant height and are better described by the Rayleigh distribution.

Design and Simulation Test of the Control System for the Automatic Unloading and Replenishment of Baskets of the 4UM-120D Electric Leafy Vegetable Harvester

This study designed a control system for the automatic unloading and replenishment of baskets based on the cooperative detection of photoelectric sensors and pressure sensors based on analyzing the structure of the 4UM-120D electric leafy vegetable harvester. The goal of this study was to increase the operation efficiency of leafy vegetable harvesters and decrease the work intensity of operators. A control system for the automatic unloading and replenishment of baskets based on the cooperative detection of a photoelectric sensor and pressure sensor was designed and constructed after an analysis of the operating principle and system components of automatic basket unloading and basket replenishment control at the rear of the harvester. The bench test results showed that the bottom photoelectric sensor and top photoelectric sensors 1 and 2 on the touch screen were not lit and the pressure sensor value was displayed as −0.00075531 kg, after pressing the system start button on the touch screen. On the touch screen, only the basket feeding motor was on: the transverse conveyor motor and the basket unloading motor were not, indicating that there was no collection basket on the unloading basket conveyor belt at this time and that the basket feeding motor was conveying an empty basket to the unloading basket conveyor belt. At 26 s, on the touch screen, only the top photoelectric sensor 2 was not on: the top photoelectric sensor 1 and the bottom photoelectric sensor were on and the pressure sensor value was shown as 1.38488 kg. Only the transverse conveyor motor lit up on the touch screen, the basket unloading motor and the basket feeding motor did not light up, indicating that the leafy vegetables temporarily stored in the transverse conveyor belt started to fall into the collection basket at this time and had not yet reached the expected capacity of the collection basket. At 43 s, the bottom photoelectric sensor and top photoelectric sensors 1 and 2 were lit on the touch screen and the pressure sensor value was shown as 2.37229 kg. On the touch screen, only the basket unloading motor lit up: the transverse conveyor motor and the basket feeding motor were not lit up, indicating that the collection basket capacity had reached the expected capacity at this time and the unloading was in progress. At 83 s, the bottom photoelectric sensor and top photoelectric sensors 1 and 2 were not lit on the touch screen and the pressure sensor value was displayed as −0.0040102 kg. On the touch screen, only the basket feeding motor lit up: the transverse conveyor motor and the basket unloading motor did not light up, indicating that the collection basket with the expected capacity had been unloaded to the ground, and the basket feeding motor was transporting empty baskets to the basket unloading conveyor belt. Through bench simulation tests, it was determined that the control system for the automatic unloading and replenishment of baskets based on the cooperative detection control strategy of the photoelectric sensor and pressure sensor reduced the probability of misjudgment and misoperation and improved system performance. This was conducted with the probability of system misjudgment and misoperation serving as the main evaluation index. The simulation results demonstrated that the control system for the automatic unloading and replenishment of baskets based on a photoelectric sensor and pressure sensor cooperative detection control strategy could be error-free judgment and avoid misoperation, effectively improving the stability, accuracy, and rapidity of the system. The study’s findings could suggest a strategy to lessen the workload of operators and increase the operational effectiveness of harvesters for leafy vegetables.

Hard-Rock κ0 at KiK-Net Sites in Japan

ABSTRACT Site-specific seismic hazard studies require the knowledge of the shear-wave velocity VS and the high-frequency site attenuation parameter κ0 at the reference rock level at depth. The latter one (called κ0,ref) is often not available and hard to derive. In this study, we make use of the KiK-net database in Japan that consists of surface and colocated downhole sensors. We select 175 sites where the bottom sensor is deployed at rock or hard-rock conditions resulting in a database with many recordings at VS≥1500 m/s. This allows us to tackle two questions: first, is it possible to derive κ0,ref from surface recordings? Second, does κ0 reach an asymptotic level at high VS that could be used as a κ0,ref in site-specific seismic hazard studies? Our results show that measures of κ0 derived from S and coda waves are equivalent. Thus, it is not possible to obtain κ0,ref from surface recordings using coda waves. On the other hand, S-wave measurements of κ0 from surface rock sites are close to κ0,ref if VS≥760 m/s or if the sedimentary cover is thin. The values of κ0 decrease with increasing VS and reach an asymptotic value. The scatter in the so obtained κ0,ref is high, but it can be reduced by selecting subregions with similar geological conditions. Finally, we observe that borehole and surface κ0 are correlated, and that the variability of κ0,ref is only slightly reduced compared to κ0 at the surface. Although we cannot exclude any influence of source effects, our findings indicate that κ0,ref has to be considered as a deep site parameter.

A differential miniaturized thickness measurement calibration system based on laser triangulation sensors

Steel plate thickness inspection is important in high-precision steel processing and manufacturing. Traditional inspection methods use manual contact measurement, which has low inspection efficiency and large human error, so we proposed a differential non-contact thickness measurement system based on laser triangulation sensors. Firstly, we designed a differential measurement structure by placing the laser triangulation sensors on the top and bottom of the steel plate to be measured, so that the data from the top and bottom sensors can be sampled and the steel plate thickness can be calculated simultaneously. Secondly, based on the principle of error averaging effect, we proposed a multi-point measurement averaging method to take multiple points on the surface of the steel plate and calculate the average value as the thickness of the steel plate. Further, to improve the measurement system’s long-term stability, the temperature drift is corrected by the polynomial fitting method. Finally, the experiment proves that the repeatability accuracy of our proposed measurement system can reach ±0.5um and the measurement drift is better than ±0.1% of the steel plate thickness.

Understanding slow-moving landslide triggering processes using low-cost passive seismic and inclinometer monitoring

Landslides are a major natural hazard, threatening communities and infrastructures worldwide. Mitigation of these hazards relies on understanding their causes and triggering processes, which critically depend on subsurface characteristics and their variations over time. In this study, we present a novel approach combining passive seismic and low-cost inclinometer monitoring methods to improve the understanding of landslide activation mechanisms and their controls. We evaluate the efficiency of this approach on a shallow, slow-moving landslide directly endangering a road bridge, a bridge that is part of an important emergency response route. Results show the value of combining the two approaches for observing and monitoring landslide hazards. Passive seismic monitoring captures the variation in soil properties (rigidity and density) over time by sensing the variations of the seismic wave velocity (dV/V and its associated correlation coefficient). At the same time, novel low-cost inclinometers are monitoring subsurface deformation (from millimetric to pluricentimetric scale) and temperature. Seismic precursors detected at the bottom sensor a few hours prior to the reactivation are followed by the reactivation of the landslide toe, releasing stresses in the top part that lead to the reactivation of the whole landslide. This reactivation occurs during an episode of heavy rainfall following a 7-month drought. Meanwhile, temperature monitoring enables us to track water infiltration and to highlight its role in the landslide mechanisms. Overall, the combination of the two monitoring methods shows promise for quantifying the sliding mechanisms of landslide reactivations and for designing landslide early warning systems.

Monolithic Three-Dimensional Integration of Carbon Nanotube Circuits and Sensors for Smart Sensing Chips.

Semiconducting carbon nanotube (CNT) film is a promising material for constructing high-performance complementary metal-oxide-semiconductor (CMOS) integrated circuits (ICs) and highly sensitive field-effect transistor (FET) bio/chemical sensors. Moreover, CNT logic transistors and sensors can be integrated through a compatible low-temperature fabrication process, providing enough thermal budget to construct monolithic three-dimensional (M3D) systems for smart sensors. However, an M3D sensing chip based on CNT film has not yet been demonstrated. In this work, we develop M3D technology to fabricate CNT CMOS ICs and CNT sensor arrays in two different layers; then, we demonstrate a preliminary M3D sensing system comprising CNT CMOS interfacing ICs in the bottom layer and CNT sensors in the upper layer through interlayer vias as links. As a typical example, a highly sensitive hydrogen sensing IC has been demonstrated to perform in situ sensing and processing functions through upper-layer FET-based hydrogen sensors exposed to the environment and bottom-layer CNT CMOS voltage-controlled oscillator (VCO) interfacing circuits. The M3D CNT sensing ICs convert hydrogen concentration information (8-128 ppm) to digital frequency information (0.78-1.11 GHz) with a sensitivity of 2.75 MHz/ppm. M3D sensing technology is expected to provide a universal sensing system for future smart sensing chips, including multitarget detection and ultralow power sensors.

Bathymetric Reconstruction From Sidescan Sonar With Deep Neural Networks

In this article, we propose a novel data-driven approach for high-resolution bathymetric reconstruction from sidescan. Sidescan sonar intensities as a function of range do contain some information about the slope of the seabed. However, that information must be inferred. In addition, the navigation system provides the estimated trajectory, and normally, the altitude along this trajectory is also available. From these, we obtain a very coarse seabed bathymetry as an input. This is then combined with the indirect but high-resolution seabed slope information from the sidescan to estimate the full bathymetry. This sparse depth could be acquired by single-beam echo sounder, Doppler velocity log, and other bottom tracking sensors or bottom tracking algorithm from sidescan itself. In our work, a fully convolutional network is used to estimate the depth contour and its aleatoric uncertainty from the sidescan images and sparse depth in an end-to-end fashion. The estimated depth is then used together with the range to calculate the point's three-dimensional location on the seafloor. A high-quality bathymetric map can be reconstructed after fusing the depth predictions and the corresponding confidence measures from the neural networks. We show the improvement of the bathymetric map gained by using sparse depths with sidescan over estimates with sidescan alone. We also show the benefit of confidence weighting when fusing multiple bathymetric estimates into a single map.

Hazard and Risk-Based Tsunami Early Warning Algorithms for Ocean Bottom Sensor S-Net System in Tohoku, Japan, Using Sequential Multiple Linear Regression

This study presents robust algorithms for tsunami early warning using synthetic tsunami wave data at ocean bottom sensor (OBS) arrays with sequential multiple linear regression. The study focuses on the Tohoku region of Japan, where an S-net OBS system (150 pressure sensors) has been deployed. To calibrate the tsunami early warning system using realistic tsunami wave profiles at the S-net stations, 4000 stochastic tsunami simulations are employed. Forecasting models are built using multiple linear regression together with sequential feature selection based on Akaike Information Criterion and knee-point method to identify sensors that improve the accuracy most significantly. The study considers tsunami wave amplitude at a nearshore location and regional tsunami loss for buildings to develop hazard-based and risk-based tsunami warning algorithms. The models identify an optimal configuration of OBS stations and waiting time for issuing tsunami warnings. The model performance is compared against a base model, which only uses the earthquake magnitude and epicenter location. The result indicates that estimating the tsunami amplitude and loss via S-net improves accuracy. For the hazard-based forecasting, adding six sensors from the S-net improves the accuracy of the estimation most significantly with an optimal waiting time of 3 min. For the risk-based forecasting, a longer waiting time between 5 and 10 min is suitable.

Hierarchical level fault detection and diagnosis of ship engine systems

As smart ships are developed, research into engine management through remote monitoring and support from data collected at onshore control centers is being conducted. However, ship engine data collected from various sensors have high dimensions, large measurement errors, few labeled data, and insufficient amounts and quality of data, making it difficult to determine the condition of engines with complex failure modes under various operating environments.This study proposes a hierarchical level fault detection and diagnosis (HL-FDD) method that combines domain knowledge of ship engines and advanced data analysis techniques. The developed method extracts key features of reduced dimensions from the original variables (sensors) through an optimal hierarchical clustering and dimension-reduction model, allowing a hierarchy divided into the top (the entire system combining all features), middle (subsystems and feature sets), and bottom (components and sensors) levels. The reduced key features are used to generate robust regression models and dynamic thresholds (prediction intervals) according to the engine load, and dynamic thresholds determine whether the engine’s condition is normal or abnormal. The dynamic thresholds are able to automatically label abnormal conditions of the engine. Once anomalies are detected at the top level, the proposed method can sequentially search for data on features belonging to the middle and bottom hierarchies for detailed fault diagnosis to determine which engine subsystem or component(s) caused the engine fault(s). Actual data collected by ship operators verify the proposed method’s efficiency, reliability, and accuracy.

Data fusion based wireless temperature monitoring system applied to intelligent greenhouse

In intelligent greenhouses, wireless sensor networks with uneven temperature distribution and low collection efficiency may lead to poor monitoring effects in real time. To improve the performance of the temperature monitoring system in intelligent greenhouses, a real-time fusion strategy for a hierarchical wireless sensor network (WSN) is proposed. The designed WSN has three layers. In the bottom, sensors collect and preprocess the temperature data of the greenhouse by an improved unscented Kalman filter. In the middle layer, each cluster-head sensor, as a local fusion center, is used to fuse the data collected from the bottom sensors by a parallel inverse covariance intersection fusion algorithm. In the top, a global fusion center is utilized to fuse the temperature data from the middle layer to reflect the global temperature of the greenhouse by an improved extreme learning machine algorithm. The designed algorithm applied in each layer ensures the efficiency and accuracy of data fusion in real time. Simulation results show that the designed fusion strategy effectively improves the fusion accuracy and realizes the real-time fusion. The performance of the designed temperature monitoring system is greatly improved.

An E-Textile Respiration Sensing System for NICU Monitoring: Design and Validation.

The world is witnessing a rising number of preterm infants who are at significant risk of medical conditions. These infants require continuous care in Neonatal Intensive Care Units (NICU). Medical parameters are continuously monitored in premature infants in the NICU using a set of wired, sticky electrodes attached to the body. Medical adhesives used on the electrodes can be harmful to the baby, causing skin injuries, discomfort, and irritation. In addition, respiration rate (RR) monitoring in the NICU faces challenges of accuracy and clinical quality because RR is extracted from electrocardiogram (ECG). This research paper presents a design and validation of a smart textile pressure sensor system that addresses the existing challenges of medical monitoring in NICU. We designed two e-textile, piezoresistive pressure sensors made of Velostat for noninvasive RR monitoring; one was hand-stitched on a mattress topper material, and the other was embroidered on a denim fabric using an industrial embroidery machine. We developed a data acquisition system for validation experiments conducted on a high-fidelity, programmable NICU baby mannequin. We designed a signal processing pipeline to convert raw time-series signals into parameters including RR, rise and fall time, and comparison metrics. The results of the experiments showed that the relative accuracies of hand-stitched sensors were 98.68 (top sensor) and 98.07 (bottom sensor), while the accuracies of embroidered sensors were 99.37 (left sensor) and 99.39 (right sensor) for the 60 BrPM test case. The presented prototype system shows promising results and demands more research on textile design, human factors, and human experimentation.

Biases in open-path carbon dioxide flux measurements: Roles of instrument surface heat exchange and analyzer temperature sensitivity

Eddy covariance (EC) measurements of ecosystem-atmosphere carbon dioxide (CO2) exchange provide the most direct assessment of the terrestrial carbon cycle. Measurement biases for open-path (OP) CO2 concentration and flux measurements have been reported for over 30 years, but their origin and appropriate correction approach remain unresolved. Here, we quantify the impacts of OP biases on carbon and radiative forcing budgets for a sub-boreal wetland. Comparison with a reference closed-path (CP) system indicates that a systematic OP flux bias (0.54μmolm−2s−1) persists for all seasons leading to a 110% overestimate of the ecosystem CO2 sink (cumulative error of 78 gC m−2). Two potential OP bias sources are considered: Sensor-path heat exchange (SPHE) and analyzer temperature sensitivity. We examined potential OP correction approaches including: i) Fast temperature measurements within the measurement path and sensor surfaces; ii) Previously published parameterizations; and iii) Optimization algorithms. The measurements revealed year-round average temperature and heat flux gradients of 2.9 °C and 16 W m−2 between the bottom sensor surfaces and atmosphere, indicating SPHE-induced OP bias. However, measured SPHE correlated poorly with the observed differences between OP and CP CO2 fluxes. While previously proposed nominally universal corrections for SPHE reduced the cumulative OP bias, they led to either systematic under-correction (by 38.1 gC m−2) or to systematic over-correction (by 17-37 gC m−2). The resulting budget errors exceeded CP random uncertainty and change the sign of the overall carbon and radiative forcing budgets. Analysis of OP calibration residuals as a function of temperature revealed a sensitivity of 5μmolm−3K−1. This temperature sensitivity causes CO2 calibration errors proportional to sample air fluctuations that can offset the observed growing season flux bias by 50%. Consequently, we call for a new OP correction framework that characterizes SPHE- and temperature-induced CO2 measurement errors.

Application of arc-shaped electrostatic sensors for monitoring the flow behaviour at top and bottom section of a pneumatic conveying pipeline

Measurement of particle’s charge in the pneumatic conveying systems can be used to monitor the flow behaviour (stable or unstable) in dilute phase pneumatic conveying. The particle’s charge can be measured by inductive electrostatic sensors and converted into a time-domain signal, which is called an electrostatic signal. However, with popular ring-shaped sensors, limited information can be obtained on electrostatic signals. Arc-shaped electrostatic sensors have some advantages on measuring particle’s charge at different locations such as the top or bottom of the pipe. To get enough information about the flow behaviour, the electrostatic signal needs to be processed by using different data analysis techniques.In this study, electrostatic signals are recorded at the top and bottom section of the pipe at varied conveying air velocity and at a constant solid feed rate. These different air velocities result in a stable dilute flow and unstable dune flow in the pipeline. The electrostatic signals of stable and unstable flows are analysed by using Fast Fourier Transform (FFT) and Hilbert-Huang Transform (HHT) techniques for real-time monitoring of flow behaviour in a pilot pneumatic conveyor.The results show that HHT analysis is far better than FFT for distinguishing between stable dilute and unstable dune flow. Where FFT only capture the difference in the flow behaviour with only top electrostatic sensor’s signal but HHT work with both top and bottom sensor’s signals. HHT does not only distinguish between stable and unstable flows but also shows the consistency in the results of the 80 ​s, 10 ​s and 5 ​s of the data analysis, which is the main requirement for any technique to be used for real-time monitoring systems. This clearly represents a huge benefit for control and monitoring in the pneumatic conveying systems.

Temperature Sequential Data Fusion Algorithm Based on Cluster Hierarchical Sensor Networks.

The process of extracting gold by biological oxidation involves oxidizing the refractory high-sulfur and high-arsenic ore with the help of bacteria to decompose the wrapping material of gold to extract the gold. Therefore, maximizing the activity of bacteria will directly affect the efficiency of gold extraction, for which it is particularly important to maintain the pulp temperature in the oxidation tank at the optimal bacteria breeding temperature. However, gold mines are generally located in mountainous areas, and the large temperature difference between day and night in winter, coupled with the influence of wind and snow, creates variations in the temperature in the oxidation tank. The traditional temperature measurement method cannot fully reflect the temperature change of the oxidation tank. As a multi-field application method, sensor information fusion can effectively address the problem of pulp temperature measurement. First, we analyzed the heat transfer principle inside the oxidation tank, and designed the cluster hierarchical sensor network according to the spatial position of each oxidation tank and the environmental interference factors. The network structure is divided into three layers; the bottom of the sensor to collect pulp temperature data shows a spiral distribution in the inner wall of the oxidation tank. Each cluster head node sensor is used as an intermediate layer to complete local measurement fusion estimation. Finally, the fusion center is taken as the upper layer to realize the global state fusion estimation. Secondly, in the data processing of the bottom temperature sensor, the traditional unscented Kalman filter (UKF) algorithm is improved and the fading memory matrix is added to improve the identification of nonlinear modeling errors. The sequential observation fusion estimator (SOFE) algorithm is embedded in the measurement update to improve the performance of local measurement fusion. Finally, in the global state fusion estimation, the sequential analysis is combined with the inverse covariance intersection, and the sequential analysis and inverse covariance intersection-global state fusion estimation (SICI-GSFE) algorithm is proposed. Through calculation and simulation, the results show that the external interference can be reduced by combining all the temperature state estimations, and the accuracy of the best global temperature state estimation is improved.

A novel temporal and spatial panorama stream processing engine on IoT applications

Abstract Nowadays, more and more streaming data are generated with the development of Internet of Things. Although, streaming data show great application values in practical scenarios, raw streams from terminal sensors are quite massive, heterogeneous and complex, and those features make it difficult for applications to deal with them. In order to simplify streaming data processing for applications, a novel temporal and spatial panorama stream processing engine is proposed. This stream processing engine forms an effective link between bottom sensors and upper stream-based applications, and provides configurable, flexible, available and usable stream services for various upper applications. With support of Internet plus and domain data, raw streaming data are formatted thorough data fusion and are represented in temporal, spatial, logic and storage views. Fusion data are encapsulated with multiple strategies and methods of clustering models on every dimension. According to configurable strategies, encapsulated data are extracted, partitioned and distributed as the form of dynamical variable gratitude data blocks into stream channels. Our engine is applied to a practical application scenario. Case study of dynamic power distribution application based on people crowd streaming data proves the customized service capabilities of our engine. And the outstanding performance of the engine is shown further by experiments evaluation in this case.

A smart capillary barrier-wick irrigation system for home gardens in arid zones

New water-conserving irrigation technologies are vital in arid countries. We investigated the effects of (i) soil substrates made of Smart Capillary Barrier Wick (SCB-W), consisting of silt loam blocks surrounded by sand-sheathes and irrigated with a sand wick cylinder (WC) as compared to a control (homogenous soil irrigated by the same wick system, HW), (ii) WC diameters (2.54 cm vs. 1.27 cm), and (iii) 2-cm sand mulch layer on soil–water dynamics during wetting–drying cycles. Field experiments with pots and HYDRUS (2D/3D) modeling were performed in two consecutive phases (with and without sand mulch). Analysis of variance at p < 0.05 was used to assess significant differences in measured water contents, θ, between the two substrates. For the wetting/drying cycles, the modeled and measured θ are in satisfactory/tolerable agreement, as documented by the model evaluation criteria, which are within acceptable ranges (the root mean squared error, RMSE 0.01–0.06; Nash–Sutcliffe coefficient, NSE 0.51–0.97, and Willmott index, d = 0.97–1). SCB-W wets the soil substrate about two times faster than HW during the wetting cycles (p < 0.05). Reducing the WC diameter prolonged the wetting time by 1 and 2 days for SCB-W and HW, respectively, the same trend of two times faster wetting of SCB-W compared to HW was maintained. SCB-W showed higher θ storage (by 44.3–52.4%) at the bottom part of the composite than HW (p < 0.05). The sand mulch layer reduced evaporation and resulted in 20 and 38.9% higher θ during the drying cycle for both the bottom and top sensors, respectively, in both substrates (p < 0.05). SCB-W could improve water conservation in home gardens.