Sensor Board Research Articles

Design of Environmental Sensor Board for Energy Harvesting: Integration of Conventional and Eco-Friendly Sensors with Power Generation Sources

Design of Environmental Sensor Board for Energy Harvesting: Integration of Conventional and Eco-Friendly Sensors with Power Generation Sources

The Peregrine Ion Trap Mass Spectrometer (PITMS) Investigation Development and Preflight Planning

The Peregrine Ion Trap Mass Spectrometer (PITMS) is a mass spectrometer instrument that operated during the Astrobotic Peregrine Mission-1 as part of the NASA Commercial Lunar Payload Services initiative. This paper describes the instrument and investigation design, development, and planning conducted by the PITMS team, consisting of a successful partnership between NASA Goddard Space Flight Center (GSFC), The Open University, NASA, and ESA. PITMS was designed to measure the abundance and temporal variability of volatile species in the near-surface lunar exosphere from a landed platform on the lunar surface. The PITMS instrument consisted of a European Space Agency–provided Exospheric Mass Spectrometer (including sensor, electronics, controller, and power supply boards) and a GSFC wrapper that provided structural elements, thermal control, and a deployable dust cover. PITMS was designed to operate as a passive sampler, where ambient gases would enter PITMS through an aperture, diffuse around the mass analyzer cavity, become ionized by electron impact and trapped in an RF field, and then sequentially be released to a detector to build a mass spectrum. PITMS was capable of measuring species with a mass-to-charge ratio (m/z) from 10 to 150 Da, with a mass resolution of approximately 0.5 amu. The PITMS science investigation was planned to be operated by GSFC with an international team of scientists. Though the mission did not achieve its lunar landing, information about the PITMS instrument and planning is provided to be able to understand and effectively use data that will be forthcoming from the investigation.

Adaptive threshold based outlier detection on IoT sensor data: A node-level perspective

The accuracy and reliability of IoT-based sensor networks depend on validating sensed data, including detecting outliers at the node level. This study proposes an online outlier detection approach using Multiple Linear Regression-based adaptive thresholds for real-time IoT/WSN sensor nodes. IoT sensors experience two outlier types: Errors, from sensor malfunctions or low battery, and Events, from sudden environmental changes. The Adaptive Threshold Based Outlier Detection (ATBOD) approach differentiates errors from events using an adaptive threshold that adjusts to real-time data patterns. Unlike existing methods that are used in literature, which lack automated model evolution and suffer from delays and high computational time, ATBOD enhances outlier detection sensitivity without increasing false alarms, which is crucial for efficient IoT sensor board operation. It also improves sensor board lifespan by discarding errors at the node level, preventing energy wastage from transmitting error data to the cloud. ATBOD outperforms existing algorithms, which are referenced for comparison, such as Enhanced Efficient Outlier Detection and Classification Approach (EEODCA), K Nearest Neighbor approximate outlier detection (KNN), and Modified Local Outlier Factor (LOF), in Error Detection Rate, Error False Positive Rate, and Energy Saving Ratio. These advancements represent a significant leap in performance, making ATBOD a superior method for real-time outlier detection in IoT sensor networks.

Point-of-Care Electrochemical Immunosensor Applied against Nosocomial Infection: Staphylococcus aureus Detection in Human Hand Skin.

Staphylococcus aureus is an important pathogen that causes nosocomial infections, resulting in unacceptable morbidity and mortality rates. In this work, we proposed the construction of a nanostructured ZnO-based electrochemical immunosensor for qualitative and semiquantitative detection of S. aureus using simple methods for growing zinc oxide nanorods (ZnO NRs) on a sensor board and immobilizing the anti-S. aureus antibody on ZnO NRs through cystamine and glutaraldehyde. The immunosensor detected S. aureus in the 103-107 colony-forming unit (CFU) mL-1 range and showed a limit of detection (LoD) around 0.792 × 103 CFU mL-1. Beyond a satisfactory LoD, the developed immunosensor presented other advantages, such as high versatility for point-of-care assays and a suitable selective factor that admits the detection of the S. aureus concentration range in human hand skin after washing. Moreover, the immunosensor showed the potential to be an excellent device to control nosocomial infection by detecting the presence of S. aureus in human hand skin.

Al Based Automatic alarm Generation and Dropping of Payload at a Particular Object through a Drone

Unmanned aerial vehicles (UAVs), commonly referred to as drones, are becoming more and more prevalent in a number of industries, including logistics, surveillance, and reconnaissance. This work introduces a novel framework that automatically generates alerts and precisely targets specific points of interest for UAV payload delivery using artificial intelligence (AI) algorithms. Our framework seamlessly integrates real-time object detection and recognition capabilities with an advanced AI-driven decision-making module to enable autonomous functionality. The operational process starts with using on board sensors and cameras to continuously monitor the environment. The AI algorithms carefully consider all the options when they identify an object of interest, such as a suspicious package or a predetermined target. They then determine the optimal course of action. If necessary, the system triggers an alert and arranges for a series of actions to enable the UAV to drop a payload at the designated location. Payload delivery mechanisms, advanced object detection algorithms, path planning algorithms that optimize UAV navigation, and deep learning models tailored for object recognition are some of the framework's key elements. The AI-powered decision-making module ensures that choices are made accurately, promptly, and with consideration for the mission's goals, the law, and the environment. A comprehensive evaluation of the proposed framework's efficacy is conducted through simulated and real-world experiments, showcasing its versatility in various contexts. The results Validate the framework's remarkable accuracy in object detection and recognition, as well as its reliable payload delivery capabilities. Furthermore, the system's autonomy lessens the need for human intervention, which makes it particularly suitable for applications that demand accuracy and speed. Our work advances the development of AI-powered unmanned aerial vehicles (UAVs) designed for automated monitoring, law enforcement, and disaster relief operations. With its great potential to improve situational awareness and operational effectiveness in a variety of environments, the proposed framework could usher in a new era of autonomous aerial system advancements.

Smart Application to Avoid Road Accidents Caused by Speed Breakers

The intelligent application presented in this study aims to stop speed breakers from causing accidents on the road. The application employs on board sensors and GPS technologies to identify speed violators and notify drivers. The software detects speed breakers and alerts drivers in real time by combining sensor data analysis and machine learning methods. Our application aims to enhance road safety by notifying drivers about speed breakers. Therefore, reducing the possibility of accidents and making the road safer for everyone

Eye Surgical Equipment Sensor Board Testing Kit

The Eye Surgical Equipment Sensor Board Testing Kit utilizes Microcontroller and Raspberry Pi to diagnose PCBs, focusing on detecting Eye Surgical Sensor Board defects impacting signal integrity. With Microcontroller’s advanced features like high-speed ADCs and flexible GPIOs, it generates and processes signals accurately. Raspberry Pi enables real-time visualization of test results. This portable and cost-effective solution is suitable for industrial applications, empowering engineers and technicians to efficiently address Eye Surgical Sensor Board defects. By leveraging the computational power of both platforms, it offers a scalable approach to Sensor Board testing, enhancing reliability and performance in high-speed digital communication systems.

Sphero Mini Sensor Board for First-Year Physics Labs

Sphero Mini Sensor Board for First-Year Physics Labs

On-Board Image Enhancement on Remote Sensing Payload

CubeSats are designed to optimize applications within the strict constraints of space and power. This paper presents an On-Board Image Enhancement technique for remote sensing payloads, focusing on achieving Auto White Balance (AWB) with limited resources and enhancing the capabilities of small/microsatellites. The study introduces hardware-based techniques, including histogram adjustment, De-Bayer processing, and AWB, all tailored to minimize hardware resource consumption on CubeSats. The integrated 1U CubeSat system comprises a sensor board, an Image Data Processor (IDP) unit, and onboard computing, with a total power consumption estimated at 2.2 W. This system facilitates image capture at a resolution of 1920 × 1200 and utilizes the proposed algorithm for image enhancement on remote sensing payloads to improve the quality of images captured in low-light environments, thereby demonstrating significant advancements in satellite image processing and object-detection capabilities.

A compact setup for behavioral studies measuring limb acceleration

Behavioral studies contribute largely to a broader understanding of human brain mechanisms and the process of learning and memory. An established method to quantify motor learning is the analysis of thumb activity. In combination with brain stimulation, the effect of various treatments on neural plasticity and motor learning can be assessed. So far, the setups for thumb abduction measurements employed consist of bulky amplifiers and digital-to-analog devices to record the data. We developed a compact hardware setup to measure acceleration data which can be integrated into a wearable, including a sensor board and a microcontroller board which can be connected to a PC via USB. Additionally, we provide two software packages including graphical user interfaces, one to communicate with the hardware and one to evaluate and process the data. This work demonstrates the construction and application of our setup at the example of thumb acceleration measurement with a custom made glove and its use for research. Using integrated circuits, the size of the measurement devices is reduced to this wearable. It is simple to construct and can be operated easily by non-technical staff.

Wearable strain sensor utilizing the synergistic effect of Ti3C2Tx MXene/AgNW nanohybrid for point-of-care respiratory monitoring

Respiratory signals are significant indicators for detecting changes in physiological conditions and early diagnosis of numerous respiratory illnesses. Herein, we have integrated a high-performance strain sensor and portable circuit board (PCB) with personalized interface to develop point-of-care respiratory monitoring device. The ultrasensitive strain sensor utilizes a conductive polymeric nanocomposite that harnesses the synergistic effect of Ti3C2Tx (MXene)/AgNW nanohybrid to develop a wearable device. The wearable device is capable of analysing pulmonary volumes, such as forced volume capacity (FVC) and forced expiratory volume (FEV1), while accurately recognizing various breathing patterns in a resting state (such as normal, forced, and obstructive), as well as during different physical activities. It shows excellent correlation (>93%) with commercial spirometer for measurement of pulmonary parameters. In addition, we present a wireless device for lab rat respiratory monitoring in anaesthetic state. The device is implemented for real-time respiratory monitoring under mild, normal and high anaesthesia doses. The levels of anaesthesia doses including a critical limit can be significantly discriminated by means of breathing frequency and amplitude, which ultimately results in saving their lives. These results demonstrate the practical feasibility of the strain sensing device as wearable electronics for point-of-care respiratory monitoring in humans and other species.

Advancements in Mobile-Based Air Pollution Detection: From Literature Review to Practical Implementation

The aim of this paper is to investigate the effectiveness of combining mobile technologies and sensors to detect harmful particles in the air and address the problem of air pollution caused by traffic and exhaust emissions. The paper contains a systematic literature review of information technology research related to pollution detection in order to point out the main obstacles in the field and propose solutions. Furthermore, the paper presents the development of an Android smartphone-based real-time monitoring system that utilizes an external analog sensor board to acquire and evaluate physical measurements. The proposed system is calibrated for CO gas measurements, and it is compared with a commercial gas analyzer instrument. Results imply that the developed system is capable of detecting concentration levels in the air and that the accuracy is within the range of the industrial device’s accuracy.

Novel 4x4 SiPM array readout with integrated preamplification stage, optimized for the PWO detectors of the EIC EEEMCal

We are reporting on a new readout circuit developed for the lead tungstate (PbWO4) scintillation detectors for the Electron Ion Collider (EIC) Electron Endcap Electromagnetic Calorimeter EEEMCal. The high magnetic field region precludes the use of photomultiplier tubes while the detector requirements specify good spectral resolution performance and fast pulse integration over a large dynamic range. We selected a silicon photomultiplier (SiPM) from Hamamatsu and produced a matrix of 4 x 4 sensors to cover the 20 mm × 20 mm scintillator coupling surface. Signal acquisition and amplification electronics boards were designed and integrated with the sensor board to produce a compact high performance readout package. A prototype detector was built and tested at Jefferson Lab with encouraging resolution and timing performance results.

Non-Destructive Thin Film Thickness Measurement based on Coplanar Capacitive Sensor and Printed Circuit Board Platform

This paper presents a compact design of a coplanar capacitive sensor based on a printed circuit board (PCB) to apply in non-contact and non-destructive thin film thickness measurement. The capacitive sensor design is structured by two coplanar capacitors, including a reference coplanar capacitor and a sensing coplanar capacitor. Using this structure, the thin film thickness could be estimated through the imbalance voltage produced between the two signals from the reference capacitor and sensing capacitor. The size of the proposed sensor is 9.99 × 9.99 mm and the capacitor electrodes structured by shapes of spiral, interdigital, and round are studied and optimized. Those electrode structures have been modelled and simulated and the simulation results reveal that the spiral structure gives the best performance. The experiment was conducted using plastic thin films with thickness ranging from 10 to 230 µm and dielectric constants ranging from 1.375 to 3.19 to investigate the working principle of the sensor. Experiment results show the approximate linearity of the output voltage corresponding to the various thickness at measurement range from 0 to 90 µm. The sensitivity of the sensor is 13.5 and 29 mV/µm corresponding to the 1.375 and 3.19 dielectric constants of the thin films, respectively. The proposed coplanar capacitive sensor is fabricated based on the PCB platform and shows good performance with a minimum cost. The experiment results demonstrate that this sensor has a high potential to be applied in several biomedical and industrial thin film thickness measurement applications.

Noninvasive hemoglobin quantification across different cohorts using a wearable diffuse reflectance spectroscopy system.

Quantifying hemoglobin is vital yet invasive through blood draws. We developed a wearable diffuse reflectance spectroscopy device comprising control and sensor boards with photodiodes and light-emitting diodes to noninvasively determine hemoglobin. Neural networks enabled recovery of optical parameters for chromophore fitting to calculate hemoglobin. Testing healthy and elderly subjects revealed strong correlation (r=0.9) between our system and invasive methods after data conversion. Bland-Altman analysis demonstrated tight 95% limits of agreement from -1.98 to 1.98 g/dL between the DRS and invasive hemoglobin concentrations. By spectroscopically isolating hemoglobin absorption, interference from melanin was overcome. Our device has the potential for future integration into wearable technology, enabling hemoglobin level tracking.

Development of non-contact ultrasonic IoT system for monitoring hardening process of concrete

The objectives of this study are to develop a non-contact ultrasonic IoT (Internet of Things) system and to investigate its applicability in monitoring the hardening process of cementitious materials. The conventional measurement methods, which include contact or embedded sensors with wires, have limited applicability on complex construction sites. Therefore, in this study, we propose the non-contact ultrasonic method for identifying the properties of cementitious materials using the characteristics of the leaky Rayleigh waves and demonstrate the effectiveness of an IoT system in improving the limitations of wired systems. The apparatus developed includes an ultrasonic transmitter, sensor board and operating software. First, we demonstrated that the non-contact ultrasonic IoT system delivers commands up to a distance of 20 m at a Wi-fi strength greater than −70 dBm. Furthermore, experimental validation was performed on three different cementitious materials (cement paste, mortar, and concrete) to investigate the applicability of the system.

Non-invasive hERG channel screening based on electrical impedance tomography and extracellular voltage activation (EIT-EVA).

hERG channel screening has been achieved based on electrical impedance tomography and extracellular voltage activation (EIT-EVA) to improve the non-invasive aspect of drug discovery. EIT-EVA screens hERG channels by considering the change in extracellular ion concentration which modifies the extracellular resistance in cell suspension. The rate of ion passing in cell suspension is calculated from the extracellular resistance Rex, which is obtained from the EIT measurement at a frequency of 500 kHz. In the experiment, non-invasive screening is applied by a novel integrated EIT-EVA printed circuit board (PCB) sensor to human embryonic kidney (HEK) 293 cells transfected with the human ether-a-go-go-related gene (hERG) ion channel, while the E-4031 antiarrhythmic drug is used for hERG channel inhibition. The extracellular resistance Rex of the HEK 293 cells suspension is measured by EIT as the hERG channels are activated by EVA over time. The Rex is reconstructed into extracellular conductivity distribution change Δσ to reflect the extracellular K+ ion concentration change Δc resulting from the activated hERG channel. Δc is increased rapidly during the hERG channel non-inhibition state while Δc is increased slower with increasing drug concentration cd. In order to evaluate the EIT-EVA system, the inhibitory ratio index (IR) was calculated based on the rate of Δc over time. Half-maximal inhibitory concentration (IC50) of 2.7 nM is obtained from the cd and IR dose-response relationship. The IR from EIT-EVA is compared with the results from the patch-clamp method, which gives R2 of 0.85. In conclusion, EIT-EVA is successfully applied to non-invasive hERG channel screening.

Ultrasonic Vibrator Glove for Enhanced Mobility and Safety in Visually Impaired Individuals

Abstract: This paper explores the conception, production, and actual use of an ultrasonic vibrator glove intended to aid the blind. The glove accomplishes real-time obstacle detection and offers tactile feedback by merging cutting-edge components such an ultrasonic sensor, haptic motor, and Arduino UNO board. For people who are blind, this breakthrough dramatically improves mobility, independence, and safety. The glove's integration of haptic vibration and ultrasonic detection overcomes conventional constraints and gives users a natural approach to understand their environment. The Ultrasonic Vibrator Glove redefines the tactile experience, allowing those who are blind to navigate their environment more successfully through quick obstacle detection and intuitive vibrations. The end result is a solution that empowers people and encourages self-assurance, independence, and security in their daily lives.

Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming

AbstractEmerging technologies are continually redefining the paradigms of smart farming and opening up avenues for more precise and informed farming practices. A tiny machine learning (TinyML)‐based framework is proposed for unmanned aerial vehicle (UAV)‐assisted smart farming applications. The practical deployment of such a framework on the UAV and bespoke internet of things (IoT) sensors which measure soil moisture and ambient environmental conditions is demonstrated. The key objective of this framework is to harness TinyML for implementing transfer learning (TL) using deep neural networks (DNNs) and long short‐term memory (LSTM) ML models. As a case study, this framework is employed to predict soil moisture content for smart agriculture applications, guiding optimal water utilisation for crops through time‐series forecasting models. To the best of authors’ knowledge, a framework which leverages UAV‐assisted TL for the edge internet of things using TinyML has not been investigated previously. The TL‐based framework employs a pre‐trained data model on different but similar applications and data domains. Not only do the authors demonstrate the practical deployment of the proposed framework but they also quantify its performance through real‐world deployment. This is accomplished by designing a custom sensor board for soil and environmental sensing which uses an ESP32 microcontroller unit. The inference metrics (i.e. inference time and accuracy) are measured for different ML model architectures on edge devices as well as other performance metrics (i.e. mean square error and coefficient of determination [R2]), while emphasising the need for balancing accuracy and processing complexity. In summary, the results show the practical feasibility of using drones to deliver TL for DNN and LSTM models to ultra‐low performance edge IoT devices for soil humidity prediction. But in general, this work also lays the foundation for further research into other applications of TinyML usage in many different aspects of smart farming.

Flexion Angle Estimation from Single Channel Forearm EMG Signals using Effective Features

Electromyography (EMG) records the electrical activity generated by skeletal muscles, offering valuable insights into muscle function and movement. To address the complexity of EMG signals, various signal analysis methods have been developed in the time and frequency domains for engineering applications like myoelectric control of prosthetics and movement analysis. In this study, EMG signals were acquired from ten healthy volunteers in different forearm positions using a Myoware Muscle Sensor and MPU6050 board. From each EMG signal, root mean square (RMS), standard deviation (STD), and mean absolute value (MAV) were computed and selected as representative features. These features were then fed into an LDA classifier to estimate forearm flexion angles. The study aims to compare the effectiveness of features calculated from the EMG signal and those derived from its discrete wavelet decomposition. The experimental results demonstrate the proposed method's efficiency in estimating forearm flexion angles using a single channel of EMG signals, achieving an average classification accuracy of 97.50 % across four gesture classes.