A Deep Learning-Based Sensor Modeling for Smart Irrigation System

The use of Internet of things (IoT)-based physical sensors to perceive the environment is a prevalent and global approach. However, one major problem is the reliability of physical sensors’ nodes, which creates difficulty in a real-time system to identify whether the physical sensor is transmitting correct values or malfunctioning due to external disturbances affecting the system, such as noise. In this paper, the use of Long Short-Term Memory (LSTM)-based neural networks is proposed as an alternate approach to address this problem. The proposed solution is tested for a smart irrigation system, where a physical sensor is replaced by a neural sensor. The Smart Irrigation System (SIS) contains several physical sensors, which transmit temperature, humidity, and soil moisture data to calculate the transpiration in a particular field. The real-world values are taken from an agriculture field, located in a field of lemons near the Ghadap Sindh province of Pakistan. The LM35 sensor is used for temperature, DHT-22 for humidity, and we designed a customized sensor in our lab for the acquisition of moisture values. The results of the experiment show that the proposed deep learning-based neural sensor predicts the real-time values with high accuracy, especially the temperature values. The humidity and moisture values are also in an acceptable range. Our results highlight the possibility of using a neural network, referred to as a neural sensor here, to complement the functioning of a physical sensor deployed in an agriculture field in order to make smart irrigation systems more reliable.

Using Sensor Graphs for Monitoring the Effect on the Performance of the OTAGO Exercise Program in Older Adults

The OTAGO exercise programme is effective in decreasing the risk for falls of older adults. This research investigated if there is an indication that the OTAGO exercise programme has a positive effect on the capacity and as well as on the performance in mobility. We used the data of the 10-months observational OTAGO pilot study with 15 (m = 1, f = 14) (pre-)frail participants aged 84.60 y (SD: 5.57 y). Motion sensors were installed in the flats of the participants and used to monitor their activity as a surrogate variable for performance. We derived a weighted directed multigraph from the physical sensor network, subtracted the weights of one day from a baseline, and used the difference in percent to quantify the change in performance. Least squares was used to compute the overall progress of the intervention (n = 9) and the control group (n = 6). In accordance with previous studies, we found indication for a positive effect of the OTAGO program on the capacity in both groups. Moreover, we found indication that the OTAGO program reduces the decline in performance of older adults in daily living. However, it is too early to conclude causalities from our findings because the data was collected during a pilot study.

Self-Protected Virtual Sensor Network for Microcontroller Fault Detection

This paper introduces a procedure to compare the functional behaviour of individual units of electronic hardware of the same type. The primary use case for this method is to estimate the functional integrity of an unknown device unit based on the behaviour of a known and proven reference unit. This method is based on the so-called virtual sensor network (VSN) approach, where the output quantity of a physical sensor measurement is replicated by a virtual model output. In the present study, this approach is extended to model the functional behaviour of electronic hardware by a neural network (NN) with Long-Short-Term-Memory (LSTM) layers to encapsulate potential time-dependence of the signals. The proposed method is illustrated and validated on measurements from a remote-controlled drone, which is operated with two variants of controller hardware: a reference controller unit and a malfunctioning counterpart. It is demonstrated that the presented approach successfully identifies and describes the unexpected behaviour of the test device. In the presented case study, the model outputs a signal sample prediction in 0.14 ms and achieves a reconstruction accuracy of the validation data with a root mean square error (RMSE) below 0.04 relative to the data range. In addition, three self-protection features (multidimensional boundary-check, Mahalanobis distance, auxiliary autoencoder NN) are introduced to gauge the certainty of the VSN model output.

A Comparison of Different Strategies for The Modification of Quartz Tuning Forks Based Mass Sensitive Sensors Using Natural Melanin Nanoparticles

Quartz tuning fork (QTF) is a measurement tool that is gaining attraction nowadays due to remarkable features like their low cost, stable resonance frequency, and considerably low working frequency. However how to functionalize a QTF as a chemical or a physical sensor is still an important problem that needs to be solved for a widespread usage. This paper describes approaches to functionalize QTFs by utilizing melanin nanoparticles (MNP) in order to create a recognition layer for the creation of a target specific mass sensitive biosensor. In order to achieve this aim, electroplating and dip coating methods are chosen for their relative ease of use and cheap operating costs for the purpose of being industry-friendly and reproducible as a product for field applications. Moreover a comparative study on chemical etching of QTFs was conducted with the goal of improving MNP attachment during dip coating process.

The Soil heat flow sensor functioning checks, imbalances’ origins and forgotten energies

Soil heat flux is an important component of the Surface Energy Balance (SEB) equation. Measuring it require an indirect measurement. Every used technique may present some possible errors tied with each specific technique, soil inhomogeneities or physicals phenomenon such as latent heat conversion beneath the plates especially in a desiccation cracking soil or vertisol. The installation place may also induce imbalances. Finally, some errors resulting from the physical sensor presence, vegetation presence or soil inhomogeneities may occur and are not avoidable. For all these reasons it is important to check the validity of the measurements. One quick and easy way is to integrate results during one year. The corresponding integration should be close to zero after a necessary geothermal heat efflux subtraction which should be included into the SEB equation for long term integrations. However, below plate evaporation and vegetation absorbed water or rainfall water the infiltration may also contribute to the observed short scale or/and long scale imbalance. Another energy source is usually not included in the SEB equation: the rainfall or irrigation. Yet its importance for a short- and long-term integration is notable. As an example, the most used sensors: Soil Heat Flux Plates (SHFP), is given.

Pitch-catch UGW-based multiple damage inference: a heterogeneous graph interpretation

Ultrasonic guided waves (UGWs) have been extensively utilized in nondestructive testing (NDT) and structural health monitoring (SHM) for detection and real-time monitoring of structural defects. By implementing multiple piezoelectric sensors onto a plane of the target structure to form a sensor network, damages within the sensing range can be detected or even visualized through a pitch-catch configuration. On the other hand, deep learning (DL) techniques have recently been widely used to aid UGW-based SHM when the waveform is over complicated to extract a specific mode of interest due to irregular structure or boundary reflections. However, not too much research work has been conducted to thoroughly combine sensor networks with DL. Existing research using DL approaches is mainly used to train and interpret waveforms from isolated sensor pairs. The topological structure of sensor layout and sensor-damagerelative positions are hardly considered in the data-driven process. Motivated by these concerns, this study offers a first-of-its-kindperspective to interpret UGW data collected from a sensor network by mapping the physical sensor-damage layout into a graph, in which sensors and potential damages serve as graph vertices bearing heterogenous properties upon coming to UGWs and the process of UGW transmission between sensors are encapsulated as wavelike messagepassing between the vertices. A novel physics-informedend-to-end GNN model, named as WaveNet, was exquisitely and meticulously developed. By utilizing wave information and topological structure, WaveNet enables inference of multiple damages in terms of severity and location with satisfactory accuracy, even when the waveforms are chaotic and the sensor arrangement is different at the training and testing stages. More importantly, beyond the SHM scenario, the present study is expected to enlighten new thinking on interconnecting physical wave propagation with virtual messaging passing in neural networks.

Real time emulator for parallel connected dual-PMSM sensorless control

<p>This paper presents a real-time emulator of a dual permanent magnet synchronous motor (PMSM) drive implemented on a field-programmable gate array (FPGA) board for supervision and observation purposes. In order to increase the reliability of the drive, a sensorless speed control method is proposed. This method allows replacing the physical sensor while guaranteeing a satisfactory operation even in faulty conditions. The novelty of the proposed approach consists of an FPGA implementation of an emulator to control the actual system. Hence, this emulator operates in real-time with actual system control in healthy or faulty mode. It gives an observation of the speed rotation in case of fault for the sake of continuity of service. The observation of the rotor position and the speed are achieved using the dSPACE DS52030D digital platform with a digital signal processor (DSP) associated with a Xilinx FPGA.</p>

Structural Digital Twin of FPSO for Monitoring the Hull and Topsides Based on Inspection Data and Load Measurement

A high-fidelity FPSO Structural Digital Twin (SDT) based on Reduced Basis Finite Element Analysis (RB-FEA) coupled with inspection data and physical sensor measurements (advisory hull monitoring system) is presented to demonstrate a complete FPSO "digital thread" that combines operational data feeds, detailed structural analysis based on as-is asset condition, and automated structural integrity reporting. This lays the groundwork for a philosophical shift for asset lifecycle management by enabling the use of "as-measured" conditions in lieu of assumed "design-conditions" for a more accurate, and robust understanding of asset health. We demonstrate the deployment of this methodology for the Bonga FPSO and discuss the value that it brings during day-to-day operations.

The Role of Unobtrusive Continuous Sensing in the Diagnosis and Management of Post-Acute Sequelae of SARS CoV-2 (Preprint)

UNSTRUCTURED Amid the COVID-19 pandemic, it has been reported that greater than 10% of patients with confirmed or suspected COVID-19 develop post-acute sequelae of SARS CoV-2 (PASC). PASC is still a disease for which preliminary medical data is being collected and pathophysiological understanding is yet in its infancy. The disease is notable for its prevalence and its variable symptom presentation and as such, diagnoses and management plans could be more holistically made if health care providers had access to unobtrusive continuous physiologic and physical sensor data at home. Such sensors would be able to provide vital sign and activity measurements that correlate directly or by proxy to documented PASC symptoms. These data can be collected at time points between hospital visits and can give care providers contextualized information from which symptom exacerbation or relieving factors may be classified. Such data can also improve the collective academic understanding of PASC by providing temporally and activity-associated symptom cataloging. In this viewpoint, we make a case for the utilization of sensor technologies that can serve as a foundation from which medical professionals and engineers may develop and pursue long-term mitigation strategies in conjunction with those ongoing in the acute setting.

Sparse Array Design for DOA Estimation of Non-Gaussian Signals: From Global Postage-Stamp Problem Perspective

In this paper, a sparse array design problem for non-Gaussian signal direction of arrival (DOA) estimation is investigated. Compared with conventional second-order cumulant- (SOC-) based methods, fourth-order cumulant- (FOC-) based methods achieve improved DOA estimation performance by utilizing all information from received non-Gaussian sources. Considering the virtual sensor location of vectorized FOC-based methods can be calculated from the second order difference coarray of sum coarray (2-DCSC) of physical sensors, it is important to devise a sparse array design principle to obtain extended degree of freedom (DOF). Based on the properties of unfolded coprime linear array (UCLA), we formulate the sparse array design problem as a global postage-stamp problem (GPSP) and then present an array design method from GPSP perspective. Specifically, for vectorized FOC-based methods, we divide the process of obtaining physical sensor location into two steps; the first step is to obtain the two consecutive second order sum coarrays (2-SC), which can be modeled as GPSP, and the solutions to GPSP can also be utilized to determine the physical sensor location sets without interelement spacing coefficients. The second step is to adjust the physical sensor sets by multiplying the appropriate coprime coefficients, which is determined by the structure of UCLA. In addition, the 2-DCSC can be calculated from physical sensors directly, and the properties of UCLA are given to confirm the degree of freedom (DOF) of the proposed geometry. Simulation results validate the effectiveness and superiority of the proposed array geometry.