A Novel Tilt and Acceleration Measurement System Based on Hall-Effect Sensors Using Neural Networks

A tilt sensor is a device used to measure the tilt on many axes of a reference point. Tilt sensors measure the bending position according to gravity and are used in many applications. Slope sensors allow easy detection of direction or slope in the air. These tilt gauges have become increasingly popular and are being adapted for a growing number of high-end applications. As an example of practical application, the tilt sensor provides valuable information about an aircraft’s vertical and horizontal tilt. This information also helps the pilot understand how to deal with obstacles during flight. In this paper, Hall-effect effective inclination and acceleration sensor design, which makes a real-time measurement, have been realized. 6 Hall-effect sensors with analog output (UGN-3503) have been used in the sensor structure. These sensors are placed in a machine, and the hall sensor outputs are continuously read according to the movement speed and direction of the sphere magnet placed in the assembly. Hall sensor outputs produce 0–5 Volt analog voltage according to the position of the magnet sphere to the sensor. It is clear that the sphere magnet moves according to the inclination of the mechanism when the mechanism is moved angularly, and the speed of movement from one point to the other changes according to the movement speed. Here, the sphere magnet moves between the hall sensors in the setup according to the ambient inclination and motion acceleration. Each sensor produces analog output values in the range of 0–5 V instantaneous according to the position of the spheroid. Generally defined, according to the sphere magnet position and movement speed, the data received from the hall sensors by the microcontroller have been sent to the computer or microcomputer unit as UART. In the next stage, the actual sensor has been removed. The angle and acceleration values have been continuously produced according to the mechanism’s movement and output as UART. Thanks to the fact that the magnet is not left idle and is fixed with springs, problems such as vibration noises and wrong movements and the magnet leaning to the very edge and being out of position even at a slight inclination are prevented. In addition, the Hall-effect sensor outputs are given to an artificial neural network (ANN), and the slope and acceleration information is estimated in the ANN by training with the data obtained from the real-time slope and accelerometer sensor.

Piezoelectric Voltage Constant and Sensitivity Enhancements Through Phase Boundary Structure Control of Lead-free (K,Na)NbO3-based ceramics

The piezoelectric voltage constant (g33) is a material parameter critical to piezoelectric voltage-type sensors for detecting vibrations or strains. Here, we report a lead-free (K,Na)NbO3 (KNN)-based piezoelectric accelerometer with voltage sensitivity enhanced by taking advantage of a high g33. To achieve a high g33, the magnitudes of piezoelectric charge constant d33 and dielectric permittivity er of KNN were best coupled by manipulating the intrinsic polymorphic phase boundaries effectively with the help of Bi-based perovskite oxide additives. For the KNN composition that derives benefit from the combination of er and d33, the value of g33 was found to be 46.9 ´ 10-3 V·m/N, which is significantly higher than those (20 - 30 ´ 10−3 V·m/N) found in well-known polycrystalline lead-based ceramics including commercial Pb(Zr,Ti)O3 (PZT). Finally, the accelerometer sensor prototype built using the modified KNN composition demonstrated higher voltage sensitivity (183 mV/g) when measuring vibrations, showing a 29% increase against the PZT-based sensor (142 mV/g).

Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network

Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.

A Revolutionary Gaming Style in Motion

From the timeline of the year, 2012 MONECT has been aiming towards the conceptuality for developing the formulation of making a virtual remote controller for a wide range of variety within the context considering various types of devices and peripherals consisting within the prospective realm of virtual controlling. Moving forward, where recently in the timeline for the year of 2017, the including of the functionality of that very same aspect with numerous advancements which was termed and computed as a remote desktop session with gaming control for a wide variety of games which includes games like Racing, Frames Per Seconds (FPS), Role-Playing Game (RPG) along with many more where each type of gaming aspect was equipped with its own perspective type of setup and a familiar type layout for the users who were considered for having different types of controllers for each specific gaming style and associated gameplay render. The project prospect evolved further within the year timeline of 2019–2021 which introduced and revolved around the rapidly deployable features and functionality with integrated advancements in terms of computing and gaming as a whole. Based on that deployment project outcome and developmental scope of the research, the application utilized the full use of the provided onboard sensors to give the user the ultimate experience while performing gameplay (for example, like the Accelerometer sensor, G-Sensor, Gyroscope sensor, Camera sensor etc. with many more). Each of the sensors controlled a different particular aspect of control. For instance, Frames Per Second (FPS) mode triggered and enabled the Gyroscope sensor which would allow the user to aim at their perspective targets for a solid headshot kill. On the other hand, the Race mode used the G-Sensor to enable steering mode of movement in the form of any vehicle. Besides that, the virtual remote sessions brought about the privilege and also gave each user a simultaneous interaction among devices and peripherals with real-time remote access at any given moment in time of usage.

What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture

Monitoring activity patterns of animals offers the opportunity to assess individual health and welfare in support of precision livestock farming. The purpose of this study was to use a triaxial accelerometer sensor to determine the diel activity of sheep on pasture. Six Perendale ewe lambs, each fitted with a neck collar mounting a triaxial accelerometer, were filmed during targeted periods of sheep activities: grazing, lying, walking, and standing. The corresponding acceleration data were fitted using a Random Forest algorithm to classify activity (=classifier). This classifier was then applied to accelerometer data from an additional 10 ewe lambs to determine their activity budgets. Each of these was fitted with a neck collar mounting an accelerometer as well as two additional accelerometers placed on a head halter and a body harness over the shoulders of the animal. These were monitored continuously for three days. A classification accuracy of 89.6% was achieved for the grazing, walking and resting activities (i.e., a new class combining lying and standing activity). Triaxial accelerometer data showed that sheep spent 64% (95% CI 55% to 74%) of daylight time grazing, with grazing at night reduced to 14% (95% CI 8% to 20%). Similar activity budgets were achieved from the halter mounted sensors, but not those on a body harness. These results are consistent with previous studies directly observing daily activity of pasture-based sheep and can be applied in a variety of contexts to investigate animal health and welfare metrics e.g., to better understand the impact that young sheep can suffer when carrying even modest burdens of parasitic nematodes.

LoRaWAN Based Manhole Cover Monitoring System

An on-line monitoring system using LoRa based wireless technology for manhole cover is proposed. The system includes sensor sensing nodes, LoRaWAN network and application. LoRaWAN based IoT has very low power consumption for long-distance transmission. We use the accelerometer sensor to monitor the position, displacement or damage of manhole covers used in sewage systems. If these covers are moved or damaged, then LoRa board alerts the authorities LoRa gateway. The gateway is connected to The Things Network (TTN), a cloud-based crowd-funded open source LoRaWAN platform. The data is uploaded to the cloud and stored, and it will alert to the maintenance department. On TTN, our application will be launched and integrated with different features such as SMS.