Sensor Module Research Articles

Optofluidic zoom system with increased field of view and less chromatic aberration.

Imaging systems are widely used in many fields. However, there is an inherent compromise between field of view (FOV) and resolution. In this paper, we propose an optofluidic zoom system with increased FOV and less chromatic aberration, which can realize switching between large FOV and high resolution. The proposed system consists of a liquid prism, a zoom objective, an image sensor and image processing module, which can realize optical zoom and deflection. The proposed system achieves non-mechanical optical zoom from f = 40.5 mm to f = 84.0 mm. Besides, the angular resolution of zoom objective is up to 26"18 at f = 84.0 mm. The deflection range is ±10°, and the whole FOV of proposed system can reach up to 30.3°. The proposed system is compact and easy to machine. In addition, we reduce chromatic aberration produced by the liquid prism significantly. The proposed system can be used in monitor system, target tracking system, telescope system and so on.

A mosquito mouthpart-like bionic neural probe

Advancements in microscale electrode technology have revolutionized the field of neuroscience and clinical applications by offering high temporal and spatial resolution of recording and stimulation. Flexible neural probes, with their mechanical compliance to brain tissue, have been shown to be superior to rigid devices in terms of stability and longevity in chronic recordings. Shuttle devices are commonly used to assist flexible probe implantation; however, the protective membrane of the brain still makes penetration difficult. Hidden damage to brain vessels during implantation is a significant risk. Inspired by the anatomy of the mosquito mouthparts, we present a biomimetic neuroprobe system that integrates high-sensitivity sensors with a high-fidelity multichannel flexible electrode array. This customizable system achieves distributed and minimally invasive implantation across brain regions. Most importantly, the system’s nonvisual monitoring capability provides an early warning detection for intracranial soft tissues, such as vessels, reducing the potential for injury during implantation. The neural probe system demonstrates exceptional sensitivity and adaptability to environmental stimuli, as well as outstanding performance in postoperative and chronic recordings. These findings suggest that our biomimetic neural-probe device offers promising potential for future applications in neuroscience and brain-machine interfaces.A mosquito mouthpart-like bionic neural probe consisting of a highly sensitive tactile sensor module, a flexible microelectrode array, and implanted modules that mimic the structure of mosquito mouthparts. The system enables distributed implantation of electrode arrays across multiple brain regions while making the implantation minimally invasive and avoiding additional dural removal. The tactile sensor array can monitor the implantation process to achieve early warning of vascular damage. The excellent postoperative short-term recording performance and long-term neural activity tracking ability demonstrate that the system is a promising tool in the field of brain-computer interfaces.

Integrated Edge Deployable Fault Diagnostic Algorithm for the Internet of Things (IoT): A Methane Sensing Application.

The Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud. Towards this, an integrated algorithm is formulated to detect fault at source and to identify the root cause element(s), based on Random Forest (RF) and Fault Tree Analysis (FTA). The RF classifier is employed to detect the fault, while the FTA is utilized to identify the source. A Methane (CH4) sensing application is used as a case-study to test the proposed system in practice. We used data from a healthy CH4 sensing node, which was injected with different forms of faults, such as sensor module faults, processor module faults and communication module faults, to assess the proposed model's performance. The proposed integrated algorithm provides better algorithm-complexity, execution time and accuracy when compared to FTA or standalone classifiers such as RF, Support Vector Machine (SVM) or K-nearest Neighbor (KNN). Metrics such as Accuracy, True Positive Rate (TPR), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), Precision and F1-score are used to rank the proposed methodology. From the field experiment, RF produced 97.27% accuracy and outperformed both SVM and KNN. Also, the suggested integrated methodology's experimental findings demonstrated a 27.73% reduced execution time with correct fault-source and less computational resource, compared to traditional FTA-detection methodology.

Soft, Sweat‐Powered Health Status Sensing and Visualization System Enabled by Laser‐Fabrication

AbstractThin and flexible skin electronics have attracted great attention for their applications in monitoring human health status continuously and intelligently. However, the versatility of these electronics is impeded by the performance of power supply and sensor modules, and their portability is also restricted by the need for external devices to handle data processing and analysis. Here, this work presents a wearable electronics system with health status sensing and visualization system (HSSVS), where the power is supplied by sweat‐activated batteries (SABs). The reported system enables the detection of crucial human physiological information, such as the Na+ concentration and the pH level in sweat, as well as skin temperature. The electrodes of the sensors and the batteries are fabricated by the laser ablation method. The laser‐induced polyimide (PI)/gelatin‐based graphene (LIGA) based sensors show a wide linear range for the sensing markers with high sensitivity, and high selectivity. The SABs based on laser‐induced PI/gelatin based graphene anchored with manganese dioxide (LIGA@MnO2) can support the entire sensing and visualization system with ultrathin, excellent biocompatibility, and mechanical properties. Additionally, incorporating a visualization design such as color changes in LEDs enables users to easily identify variations in health status. This integrated system demonstrates promising potential in smart sensing devices for health management.

Efficient IoT-machine learning assisted heart failure prediction using adaptive fuzzy-based LSTM-RNN algorithm

Chronic diseases like diabetes, Heart Failure (HF), malignancy, and severe respiratory sickness are the leading cause of mortality around the globe. Dissimilar indications or traits are extremely difficult to identify in HF patients. IoT solutions are becoming increasingly commonplace as smart wearable gadgets become more popular. Sudden heart attacks have a short life expectancy, which is terrible. As a result, a patient monitoring of heart patients based on IoT-centered Machine Learning (ML) is presented to help with HF prediction, and treatment is administered as necessary. Verification, Encryption, and Categorization are the three phases that make up this developed model. Initially, the datasets from the IoT sensor gadget are gathered by authenticating with a specific hospital through encryption. The patient’s integrated IoT sensor module then transfers sensing information to the cloud. The Improved Blowfish Encryption (IBE) approach is used to protect the sensor data transfer to the cloud. Then the encrypted data is decrypted, and the classification is performed using the Adaptive Fuzzy-Based Long Short-Term Memory with Recurrent Neural Network (AF-LSTM-RNN) algorithm. The results are classed as malignant or benign. It assesses the patient’s cardiac state and sends an alert text to the doctor for treatment. The AF-LSTM-RNN-based HF prediction outperforms the existing techniques. Accuracy, sensitivity, specificity, precision, F-measure and Matthews Correlation Coefficient (MCC) are compared to existing procedures to ensure the planned research is genuine. Using the Origin tool, these metrics are shown as research findings.

Real-Time Monitoring of Human Breathing Using Wearable Tilted Fiber Grating Curvature Sensors

Respiration rate and volume are important physiological indicators of human health. They can indicate cardiovascular health status and cardiopulmonary function. Here, we demonstrate a compact breath monitoring system based on a wearable tilted fiber Bragg grating (TFBG) curvature sensor for human respiration monitoring. With the assistance of a custom designed breathing tube, TFBGs provide a very reproducible highly sensitive, real time response to curvature changes during respiration. The miniaturized and cost effective sensing system which we propose consists of a tunable laser module, a field programmable gate array acquisition control circuit module, and a sensor module. Different breathing patterns can be successfully identified from the optical amplitude and frequency output of the sensor. The robust instrument displays high repeatability and real-time tracking.

Enhancement of a pyroelectric body energy harvesting scheme employing pulsed electric fields.

This research utilizes waste heat energy as a sustainable energy source to enhance pyroelectric power output by combining pyroelectric nanogenerators with an external pulsed electric field. When the surface temperature of the pyroelectric body varies, applying different pulses of the external electric field results in maximum power accumulation. A novel power-generating experimental setup was developed to measure and compute pyroelectric power generation. A standard Fuji ceramic C-9 sample was used to generate pyroelectric energy in a 20 °C temperature range from 120 to 140 °C. The continuous temperature variation frequency was 0.05Hz, and the pulsed electric field was applied when the temperature rose. Pulses of the electric field with widths of 10, 50, 100, and 200ms were applied to the sample under different pulse amplitudes, and the amplitude of each pulse was 250, 500, 1000, or 1500 V/mm. The maximum power generated through the application of an external pulsed electric field under the above-mentioned conditions was evaluated. This system had the highest power density of 0.204 mJ cm-2 °C-1kV-1. In addition, for the lowest input power, the maximum power generation condition was a 10ms pulse width and an amplitude of 250Vmm-1 in the applied electric field. This state might power smart sensor modules, IoT devices, automobiles, and other waste heat energy applications. Nano-pulse electric field applications may reduce input power to its lowest level, dependent on net-producing power. Therefore, new researchers can use net-generation power efficiency to create a large-scale power source using multiple pyroelectric arrays.

On Wireless Sensor Network Models: A Cross-Layer Systematic Review

Wireless sensor networks (WSNs) have been adopted in many fields of application, such as industrial, civil, smart cities, health, and the surveillance domain, to name a few. Fateway and sensor nodes conform to WSN, and each node integrates processor, communication, sensor, and power supply modules, sending and receiving information of a covered area across a propagation medium. Given the increasing complexity of a WSN system, and in an effort to understand, comprehend and analyze an entire WSN, different metrics are used to characterize the performance of the network. To reduce the complexity of the WSN architecture, different approaches and techniques are implemented to capture (model) the properties and behavior of particular aspects of the system. Based on these WSN models, many research works propose solutions to the problem of abstracting and exporting network functionalities and capabilities to the final user. Modeling an entire WSN is a difficult task for researchers since they must consider all of the constraints that affect network metrics, devices and system administration, holistically, and the models developed in different research works are currently focused only on a specific network layer (physical, link, or transport layer), making the estimation of the WSN behavior a very difficult task. In this context, we present a systematic and comprehensive review focused on identifying the existing WSN models, classified into three main areas (node, network, and system-level) and their corresponding challenges. This review summarizes and analyzes the available literature, which allows for the general understanding of WSN modeling in a holistic view, using a proposed taxonomy and consolidating the research trends and open challenges in the area.

Arduino tabanlı uygun maliyetli ve taşınabilir bir lüksmetre tasarımı

Light is important physiological effects that determine the development of vegetation. Light intensity and measurements are important for agriculture and livestock. In this study, a cost effective and portable luxmeter was developed for agricultural purposes using Arduino and GY-30 sensor module and compared with a commercial device. The regression results between 0.945 and 0.969 (very high) obtained in study were in agreement with the recent studies It was understood that the developed system showed results compatible with commercial luxmeter devices.

Low-cost IoT-Based Smart Notification System for Rural Agriculture

At present, people in the countryside use a manual agricultural machine for harvesting. This contributes to global warming and agricultural safety problems as manual irrigation increases farmers' time on the farm. The use of manual machinery causes smoke during irrigation, which increases the heating of the earth's surface and thus reduces the green energy effect. So they do not spend a lot of money on buying crude oil when they use the system. On the other hand, there are bandits who come into conflict with farmers and animals that attack farmers in the bush, which makes it difficult for farmers to reach their farms. A simple but effective IoT system, as we have proposed in this study, helps to solve these problems to a large extent. In this study, we use a combination of a Raspberry Pi as the main processor and sensor modules as devices to collect agricultural data for further insights and analysis. Apart from automatic irrigation through the power-saving water pump, the developed systems have taken the initiative to reduce the time farmers have to spend on the farm and provide instant notifications to a mobile device at any time and place.

Application of vibration signal detection in mine hoist fault monitoring system

Due to the harsh working environment and various types of faults in mine hoists, the implementation of conventional detection is relatively difficult. In order to improve the maintenance and fault detection efficiency of mine hoist, a portable vibration detection system is designed and applied in this paper, which can judge the type and characteristics of mechanical faults according to the different responses of vibration signals. According to the test conditions of the vibration system, the overall structure and functional composition of the hardware system are established. The key functional modules are designed, including signal conditioning module, main control module, vibration sensor and power management module. The main reasons that affect and produce the hoist fault and mechanical vibration are analyzed. The wavelet packet analysis method is proposed to realize the decomposition of low frequency and high frequency signals to improve the signal resolution of the whole frequency band in the frequency domain. The vibration test system has the function of real-time data transmission and control. The realization methods include WiFi and reserved RS485 communication interface, which can match the communication rate with the data sampling rate. The amplifying circuit of the electric signal is designed to effectively ensure that the output voltage of the charge amplifier is within the input range of the amplified ADC value. In order to verify the working effect of the system, the most common gearbox and bearing failures are taken as examples. The response signals of vibration test and diagnosis can be effectively obtained, and the fault types and characteristics can be accurately judged. Through confusion matrix analysis, the reliability of the system detection index is effectively verified. The research results show that the data of the vibration detection system is stable and reliable, and can achieve good troubleshooting effect. It also can be known that different fault types can excite different vibration responses, especially at resonance frequencies. According to the verification, the data of the vibration detection system is stable and reliable, and can achieve good troubleshooting effect.

Garbage Segregation and Monitoring Using Low-Cost IoT System for Smart Waste Management

Waste management is one of the serious issues in maintaining the longevity of life. Managing waste in garbage bins not only requires a systematic garbage segregation policy but also a prevention and control mechanism for human behaviour. With the aid of the low-cost-budget Internet of Things (IoT) approach, managing garbage bins for municipal and town councils become practical, effective, and easy to be enforced. This study proposed a proof-of-concept prototype of a low-cost IoT approach using Raspberry Pi and cheap sensor modules to segregate waste into garbage bins with local alarming and remote notification systems. The systems are capable of detecting metal and non-metal waste so that segregation can be applied, and measuring waste levels in the garbage bin to prevent waste overflow. On top of that, the systems are also able to warn with loudness sound of a mistype waste scanned at the bin lid and send prompt notifications to the remote observer for further actions. The study results from a number of performance testing demonstrated the developed systems can accurately measure waste levels either to be empty, half-filled, or 80% filled when the ultrasonic ranging module sensor is placed 105° inside the bin. This is the best positioning we recommended when working with a bin of size 15 cm in height and 10 cm in width. Finally, we observed that the finest detection results were achieved when the movement of objects are scanned less than 2 cm, but not more than 4 cm away.

Ultrasensitive recognition performance of a folded π-based 1,8-naphthalimide-phthalocyanine fluorescence sensor

Designing ultrasensitive and smart molecules as molecular switches is highly valued in molecular recognition. In this work, a folded π-based molecular switch (NAPC) containing 1,8-naphthalimide and zinc(II) phthalocyanine functional modules was synthesized via cyclo tetramerization of four synthesized 1,8-naphthalimide sensors (NA). The photophysical properties of the folded molecular switch were investigated. This peripherally 1,8-naphthalimide substituted zinc(II) phthalocyanine possessed superior sensitivity in near-infrared “off-on” detection of Al3+, Fe3+, and Cr3+ compared with NA, and the detection limits were calculated to be 0.077 μmol/L for Al3+, 0.073 μmol/L for Fe3+ and 0.067 μmol/L for Cr3+, which were far less than that of NA. Due to the sensitive detection of Fe3+, NAPC was also applied in detecting NO2−. The ultrasensitive recognition mechanism of NAPC was determined to be the level-by-level linkage in the molecule in which the photo-induced electron transfer process in 1,8-naphthalimide sensor module was inhibited by complexing with metal ions first and then caused the inhibition of π-π stacking between two modules. This new folded molecule with a cascade linkage effect has the potential as an excellent fluorescence switch that could be applied in molecular recognition and environmental management.

Perancangan Prototipe Rain Drop Sensor Berbasis Arduino Uno

In Indonesia, the climate is mostly hot and humid, and most of the rainfall occurs. As a result, people are less aware of how much rain occurs inside rooms, homes, and offices. We need a warning to explain the problem so that we can take the necessary action when it rains. By creating Arduino-based rain sensors and implementing the fourth industrial revolution in everyday life, the study aims to raise awareness of rain unknowingly. The initial method is the implementation of the Raindrop Sensor based arduino which is an Arduino standard board based on the ATmega328P microcontroller. The Rain Drop Sensor Module works when it rains, when the electric vibration buzzer rains into sound vibration, then the buzzer will provide sound information. The research results of the sensor value of more than 500 with the green light indicator lighting the Buzzer does not emit noise then the conditions are not rainy or can be meant the weather is medium bright, then the sensor value is less than 500 by the indicator Buzzer on then emits the sound and the red lighting indicator color lighting, then indicates the rain condition.

Accident Detection and Alert System Using IoT

Accidents have recently emerged as a major source of concern for humans. Due to a lack of information about the location, magnitude of the accident (or) other issues, some victims cannot be rescued by the response team, which may be an ambulance or police. Many technologies were developed, however they were unable to overcome the problem. However, this project will provide clarification on how to overcome this hurdle. A central system is employed in this project, to which a vibrator sensor, GSM (Global System for Mobile Communications), and GPS modules are attached. To prevent detecting errors, numerous sensors are strategically distributed throughout the vehicle. After an accident takes place the vibrator sensor becomes active, and GPS (Global Positioning System) is engaged with the assistance of the central system GSM ( Global System for Mobiles ). Immediately, alert messages will be handed down to the response teams, outlining the live location and severity of the accident

Soil Fertility Detection and Crop Prediction using IoT and Machine Learning

India has huge agriculture heritage. This is the major source of livelihood for most Indian families. Farmers are seen to use fertilizers in inappropriate proportion to enhance crop yield which results in infertile land. To overcome this issue, to check the fertility level of the soil, environment conditions and predicting suitable crop and fertilizers required is need of hour. Soil fertility depends on nutrients like Nitrogen (N), Phosphorous (P), and Potassium (K). It is also affected by environmental factors such as temperature, moisture, humidity, etc. The proposed system provides a cost-effective solution using IoT and Machine Learning based approach to check the NPK concentration present in the soil. Based on which, user can predict the soil suitable crop. The technique used comprises an integrated light transmission and detection system which consists of three LEDs with different wavelengths. Photodiode (LDR sensor module) is used for light detection purposes. The output obtained from the photodiode is handled using a Arduino UNO microcontroller. Based on the inputs received from LDR module, NPK concentration can be evaluated. The model is trained with the Crop Prediction dataset to predict the crop using LightGBM algorithm. The proportion of NPK nutrients and the predicted crop is sent to the user as a text message through the GSM module and ThingSpeak cloud platform.

The silicon photomultiplier-based camera for the Cherenkov Telescope Array small-sized telescopes

The Cherenkov Telescope Array (CTA) is the next generation ground-based gamma-ray astronomy observatory, planned to comprise two arrays of imaging air Cherenkov telescopes (IACTs) located in the northern and southern hemispheres. Three telescope sizes are required to cover the CTA gamma-ray energy range from 20 GeV to 300 TeV. An array of several tens of Small-Sized Telescopes (SSTs) at the southern site situated in the Andes at Paranal in Chile, will provide unprecedented sensitivity above 1 TeV and up to 300 TeV, and offer the highest angular resolution of any instrument at these energies. Following a down selection from three prototype telescopes, the design finally selected for SST comprises a dual mirror Schwarzschild–Couder optic with a 4.3 m diameter primary mirror and a 1.8 m secondary mirror imaged by a silicon photomultiplier (SiPM)-based camera with a ∼9°field of view (FoV). The dual mirror optics produce a smaller plate-scale aplanatic focal plane allowing a small, low-cost camera to be employed, compared to that required for the conventional single mirror Davies-Cotton IACT design. The camera comprises an array of 2048 SiPM pixels, configured as 32 sensor and electronics modules each with an 8 × 8 pixel2 tile populated with 6 × 6 mm2 SiPM pixels. Full waveform capture on every channel is provided by the TARGET ASIC which performs the dual function of event triggering and waveform digitization of the full camera at 1 GSample/s. We describe the finalized SST camera design including its optimization for the production phase of the project anticipated to begin in 2023.

Perancangan Alat Penentuan Koefisien Gesek Statis Menggunakan Sensor IR FC-51 dan Potensiometer Berbasis Arduino Uno

An Arduino Uno-based static friction coefficient (µs) determination tool equipped with an IR sensor module FC-51 has been created. This µs determination tool is made to reduce the possibility of observation errors when doing physics labs. The µs determination tool consists of an FC-51 IR sensor component to detect precisely when an object is moving. The potentiometer measures the angle of the base surface, and an Arduino Uno device transmits data from the hardware to the computer in real time. The work test of the µs determination tool starts with the base surface with a slope of 0o, and then the object is placed on the base surface and precisely above the surface of the FC-51 IR sensor. When the base surface is tilted slowly, the potentiometer also rotates, showing the plane angle's size. As long as the object is not moving, the IR FC-51 sensor is in low condition. When the object moves, the IR FC-51 sensor is in high condition. The change in signal from low to high indicates that the object is moving when the plane reaches the slope angle, and the slope angle shows the value of µs. The µs are measured on a wooden base with wooden blocks and a glass base with glass blocks. The results obtained for a wooden base with wooden blocks µs = 0.73 with an average slope angle = 36.16o and a glass base with glass blocks µs = 0.4 with an average slope angle = 22.1o. The results show that the µs level of the wood surface with wood is greater than the µs level of the glass surface with glass.

Size‐dependent failure behavior of commercially available lithium‐iron phosphate battery under mechanical abuse

AbstractUnder mechanical abuse, the failure of lithium‐ion batteries occurs in various stages, characterized by different force, temperature, and voltage responses, and require in situ measurements for analysis. First, four sizes of commercially available lithium‐iron phosphate batteries (LFPB), namely 18650, 22650, 26650, and 32650, were subjected to quasistatic lateral and longitudinal compression and nail penetration tests. The failure, characterized by the voltage drop and temperature rise at the onset of the first internal short‐circuit (ISC), was identified by an Aurdino‐based voltage sensor module and a temperature measurement module, respectively. The battery failure load and peak temperature at the onset of ISC were found to rely strongly on the battery size. The failure was delayed for small‐sized 18650 batteries during lateral compression, unlike in longitudinal compression and nail penetration tests. At the onset of ISC, the temperature rise above the ambient value was different for different LFPBs. It was found to be maximum (36.4°C) for LFPB 32650 under longitudinal compression and minimum (1.5°C) under lateral compression tests among the considered geometries. Further, LFPB 26650 exhibited a balanced thermal behavior during the test. Such thermal response can be sensed timely for effective thermal management of lithium‐ion batteries.

Scalable, modular continuous wave functional near-infrared spectroscopy system (Spotlight).

We present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules-each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature. Spotlight's goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain-computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research. We report sensor characteristics in system validation on phantoms and motor cortical hemodynamic responses in a human finger-tapping experiment, where subjects wore custom 3D-printed caps with two sensor modules. The task conditions can be decoded offline with a median accuracy of 69.6%, reaching 94.7% for the best subject, and at a comparable accuracy in real time for a subset of subjects. We quantified how well the custom caps fitted to each subject and observed that better fit leads to more observed task-dependent hemodynamic response and better decoding accuracy. The advances presented here should serve to make fNIRS more accessible for BCI applications.