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.

Verification in Relevant Environment of a Physics-Based Synthetic Sensor for Flow Angle Estimation

In the area of synthetic sensors for flow angle estimation, the present work aims to describe the verification in a relevant environment of a physics-based approach using a dedicated technological demonstrator. The flow angle synthetic solution is based on a model-free, or physics-based, scheme and, therefore, it is applicable to any flying body. The demonstrator also encompasses physical sensors that provide all the necessary inputs to the synthetic sensors to estimate the angle-of-attack and the angle-of-sideslip. The uncertainty budgets of the physical sensors are evaluated to corrupt the flight simulator data with the aim of reproducing a realistic scenario to verify the synthetic sensors. The proposed approach for the flow angle estimation is suitable for modern and future aircraft, such as drones and urban mobility air vehicles. The results presented in this work show that the proposed approach can be effective in relevant scenarios even though some limitations can arise.

Incorporating Fluorescent Nanomaterials in Organically Modified Sol-Gel Materials – Creating Single Composite Optical pH Sensors

Optical sensors hold the promise of providing the coupling between the tangible and the digital world that we are currently experiencing with physical sensors. The core of optical sensor development...

A Sliding Window Data Compression Method for Spatial-Time DOA Estimation

This paper presents a sliding window data compression method for spatial-time direction-of-arrival (DOA) estimation using coprime array. The signal model is firstly formulated by jointly using the temporal and spatial information of the impinging sources. Then, a sliding window data compression processing is performed on the array output matrix to realize fast calculation of time average function, and the computational burden has been reduced accordingly. Based on the concept of sum and difference co-array (SDCA), the vectorized conjugate augmented MUSIC is adopted, with which more sources than twice of the physical sensors can be resolved. Additionally, the sparse array robustness to sensor failure has been evaluated by introducing the concept of essential sensors. The theoretical analysis and numerical simulations are provided to confirm the effectiveness performance of the proposed method.

Indirect Reconstruction of Structural Responses Based on Transmissibility Concept and Matrix Regularization

A novel method is proposed based on the transmissibility concept and matrix regularization for indirectly measuring the structural responses. The inputs are some measured responses that are obtained via physical sensors. The outputs are the structural responses corresponding to some critical locations where no physical sensors are installed. Firstly, the transmissibility concept is introduced for expressing the relationship between the measured responses and the indirectly measured ones. Herein, a transmissibility matrix is formulated according to the theory of force identification under unknown initial conditions. Then, in order to reduce the size of the transmissibility matrix, structural responses are reshaped in a form of a matrix by using the concept of moving time windows. According to the matrix form of input-output relationship, indirect reconstruction of responses is boiled down to an optimization equation. Since inverse problem may be ill-conditioned, matrix regularization such as F-norm regularization is then recommended for improving the optimization problem. Herein, the penalty function is defined by using a weighted sum of two F-norm values, which correspond to the estimated responses of physical sensors and the ones of the concerned critical locations, respectively. Numerical simulations and experimental studies are finally carried out for verifying the effectiveness and feasibility of the proposed method. Some results show that the proposed method can be applied for indirectly measuring the responses with good robustness.

Editorial of Special Issue “Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans”

The combination of physical sensors and computational models to provide additional information about system states, inputs and/or parameters, in what is known as virtual sensing, is becoming more and more popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, robotics and human biomechanics sectors [...]

A Review of Digital Innovations for Diet Monitoring and Precision Nutrition

This article provides an up-to-date review of technological advances in 3 key areas related to diet monitoring and precision nutrition. First, we review developments in mobile applications, with a focus on food photography and artificial intelligence to facilitate the process of diet monitoring. Second, we review advances in 2 types of wearable and handheld sensors that can potentially be used to fully automate certain aspects of diet logging: physical sensors to detect moments of dietary intake, and chemical sensors to estimate the composition of diets and meals. Finally, we review new programs that can generate personalized/precision nutrition recommendations based on measurements of gut microbiota and continuous glucose monitors with artificial intelligence. The article concludes with a discussion of potential pitfalls of some of these technologies.

Usage of Arduino-based Datalogger for thermal comfort variables monitoring

Environmental monitoring is an effective tool to identify problems in anthropic areas, and the emergence of cyber-physical sensors contributes to technological advances in the area. This paper introduces a device based on the Arduino cyber-physical platform to monitor air temperature and relative humidity in real-time with high efficiency. With the relationship between these two environmental variables, it will be possible to calculate the Heat Index (CI), the Temperature and Humidity Index (ITU), the Effective Temperature Index (ET), and the Thermal Discomfort Index (IDT). The Datalogger developed is easily programmable and easy to assemble and presented stable operation and proper functioning.