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.

Investigation of Fractal Carbon Nanotube Networks for Biophilic Neural Sensing Applications

We propose a carbon-nanotube-based neural sensor designed to exploit the electrical sensitivity of an inhomogeneous fractal network of conducting channels. This network forms the active layer of a multi-electrode field effect transistor that in future applications will be gated by the electrical potential associated with neuronal signals. Using a combination of simulated and fabricated networks, we show that thin films of randomly-arranged carbon nanotubes (CNTs) self-assemble into a network featuring statistical fractal characteristics. The extent to which the network’s non-linear responses will generate a superior detection of the neuron’s signal is expected to depend on both the CNT electrical properties and the geometric properties of the assembled network. We therefore perform exploratory experiments that use metallic gates to mimic the potentials generated by neurons. We demonstrate that the fractal scaling properties of the network, along with their intrinsic asymmetry, generate electrical signatures that depend on the potential’s location. We discuss how these properties can be exploited for future neural sensors.

Research on Navigation and Positioning Technology of Intelligent Accompanying Tool Car in Dispatching Automation Computer Room

In view of the wide variety of equipment in the dispatch automation machine room and the dense arrangement of cabinets, a navigation technology suitable for the dispatch automation machine room is proposed on the intelligent companion tool cart. In this paper, three sensors of ultrasonic, infrared and lidar are designed to form an intelligent neural sensor, and the information received by the intelligent neural sensor is calculated to generate a vector map through a software algorithm. At the same time, a plane coordinate network is established. The grid coordinate unit accuracy is 0.1cm. At the same time, the concept of “virtual fence” was put forward to fix the workers at the working point, which increased the safety of work. Subsequently, an experimental test of positioning and navigation of the tool cart was carried out. The experimental results showed that the tool cart can accurately locate and generate a vector map with an accuracy error of less than 10cm. The navigation method has a good application effect and has a good promotion value.