Different Types Of Sensors Research Articles

A Low-Cost Underground Mining and Miners Monitoring System Using Internet of Things

The safety of mine workers is a serious worry nowadays. the miners construct underground rooms to facilitate the minerals to be taken out of the mine at work in, which requires greater output and a larger workforce. In underground mining work locations, 2753 injuries were reported as non-fatal lost- time, resulting in 190,005 lost workdays. The main aim of this proposed system is to save workers from sudden falling and detect the toxic gases present in the mining area. Using the IOT technology, we created a system with different types of sensors to solve these issues. We used flame sensor, temperature and humidity sensor and Gas sensor, to detect the toxic gas environment inside the mining and detect the fire burst inside mining in the first module. Accelerometer sensor is used to detect the falling of the worker and the pulse sensor is used to detect the heartbeat of the worker in the second module. We have created the two modules where one module is for miners monitoring and another is for mining monitoring All these sensors are integrated with the NodeMCU. All the obtained data is sent to thingspeak cloud and if any abnormality is detected we will receive a notification through email using alert API.

Deep Neural Networks-Enabled Vehicle Detection Using High-Resolution Automotive Radar Imaging

Advanced driver assistance systems (ADASs) and autonomous vehicles rely on different types of sensors, such as camera, radar, ultrasonic, and LiDAR to sense the surrounding environment. Compared with the other types of sensors, millimeter-wave automotive radar has advantages in terms of cost and reliability under bad weather conditions (e.g., snow, rain, fog) and doesn't suffer from light condition variations (e.g., darkness). Typical radar devices used in today's commercial vehicles with ADAS features produce sparse point clouds in low angular resolution with a limited number of antennas. In this paper, we present a machine learning-aided signal processing chain to suppress the radar imaging blur effect introduced by the phase migration in time-division multiplexing multi-input multi-output (MIMO) radar, to generate low-level high-resolution radar bird's eye view (BEV) spectra with rich object's features. Compared with radar point clouds, there is no information loss in radar BEV spectra. We then propose a Temporal-fusion, Distance tolerant single stage object detection Network, termed as, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TDRadarNet</i> , and an enhanced version, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TDRadarNet+</i> to robustly detect vehicles in both long and short ranges on radar BEVs. We introduce a first-of-its-kind multi-model dataset, containing 14,800 frames of high-resolution low-level radar BEV spectra with synchronized stereo cameras RGB images and three-dimensional (3D) LiDAR point clouds. Our dataset achieves 0.39 m range resolution and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.2^\circ$</tex-math></inline-formula> degree azimuth angular resolution with 100 meters maximum detectable range. Moreover, we create a sub-dataset, the Doppler Unfolding dataset, containing 244,140 beam vectors extracted from the 3D radar data cube. With extensive testing and evaluation, we demonstrate that our Doppler unfolding network achieves 93.46% Doppler unfolding accuracy. Compared to YOLOv7, a state-of-the-art image-based object detection network, TDRadarNet achieves a 70.3% Average Precision (AP) for vehicle detection, demonstrating a 21.0% improvement; TDRadarNet+ achieves a 73.9% AP, showing a 24.6% improvement in performance.

Drift of MEMS Capacitive Accelerometers for One-Year Room-Temperature Storage: A Simulation and Experimental Study

In this study, the drift of microelectromechanical system (MEMS) accelerometers stored at room temperature for one year is investigated by both finite-element analysis (FEA) simulation and experiments. In the simulation, a comprehensive FEA model considering the die-attach adhesive viscoelasticity was established to emulate the deformation of the sensor package structure during storage. Then drift models of the scale factor and bias of the accelerometer were built according to the deformed structure. To validate the simulation results, several accelerometers were packaged and their outputs were tested and recorded once per month. The two parameters displayed a continuous drift owing to the release of the packaging stress in the chip. The scale factor drifts were negative and about 2500 ppm for different types of sensors. However, the bias drifts were positive or negative, and their magnitudes were ranging from 12 to 32 mg in storage. Moreover, they reached a steady state after about ten months (< 50 ppm per month for the scale factor) and six months (< 1 mg per month for bias) of room-temperature storage. The results in this article provide meaningful guidance for the application of MEMS accelerometers.

The Role of IoT in Woman’s Safety: A Systematic Literature Review

Women’s safety has been highlighted as one of the major concerns of any society where several women are dealing with various safety issues like harassment, rape, molestation, and domestic violence due to different social or cultural reasons. Internet of Things (IoT) is becoming a promising technology to support day-to-day concerns and provide support in handling various affairs. Many IoT-based devices have been introduced by the community to help women deal with their potential safety threats. This study presents a systematic literature review of research studies exhibiting the IoT devices for women’s safety, the main features these devices offer as well as the wearable, sensors used, and the machine learning algorithms used. The review is carried out by carefully examining and synthesizing the research articles published between 2016 to 2022 in well-reputed research venues. The results revealed that different types of sensors are used to capture the state of women undergoing safety issues where the pulse-rate, and pressure sensors are most commonly used sensors in these devices. In addition, the devices used different technology to transmit the alerts including global positing system (GPS), global system for mobile communication (GSM), and Raspberry pi. Furthermore, several machine learning algorithms such as logistic regression, hidden Markov, and decision trees are used to identify the potential under threat women and help prevent the undesirable situation for women beforehand. It was identified that despite producing notable research in the underlying domain the systems emphasizing auto-activation of alert generation with lesser human interaction and improved accuracies are required to be developed for effectively addressing the concern. In addition to reviewing the literature, this study suggests a taxonomy posing different techniques, features, wearables, and sensors used in IoT-based women safety devices. Furthermore, the gaps and challenges pertaining to the IoT devices and their usability for women’s safety have also been highlighted. In addition, this work proposes an architectural model that presents prominent components necessary to develop IoT-based women’s safety devices. Lastly, this study emphasizes the use of combinations of sensors to get multiple types of input data that could lead to determining the possibility of threat with better accuracies and precisions.

In-Situ Measuring sEMG and Muscle Shape Change with a Flexible and Stretchable Hybrid Sensor for Hand Gesture Recognition.

The accurate recognition of hand motion intentions is an essential prerequisite for efficient human-machine interaction (HMI) systems such as multifunctional prostheses and rehabilitation robots. Surface electromyography (sEMG) signals and muscle shape change (MSC) signals which are usually detected with different types of sensors have been used for human hand motion intention recognition. However, using different sensors to measure sEMG and MSC respectively, it would be inconvenient and add deploying difficulty for human-machine interaction systems. In this study, a novel flexible and stretchable sensor was fabricated with a nano gold conductive material, which could simultaneously sense both sEMG and MSC signals. Accordingly, a wireless signal acquisition device was developed to record both sEMG and MSC signals with the fabricated hybrid sensors. The performance of the proposed in-situ dual-mode signal measurement (IDSM) system was evaluated by the recording signal quality and the accuracy of hand gesture recognition. The results demonstrated that by using two pairs of the hybrid sensors, the proposed IDSM system could obtain two-channel sEMG at a noise level of about 0.89 μVrms and four-channel MSC with a resolution of about 0.1 Ω. For a recognition task of 11 classes of hand gestures, the results showed that only with two pairs of the hybrid sensors, the average accuracy over all the subjects was 95.6 ± 2.9%, which was about 7% higher than that with two-channel sEMG and six-channel accelerometer signals. These results suggest that the proposed IDSM method would be an efficient way to simplify the human-machine interaction system with fewer sensors for high recognition accuracy of hand motions.

Analiza rada CAN komunikacije korišćenjem različitih vrsta senzora

This paper presents the analysis of CAN communication protocol operation which actually is a communication between the nodes utilizing different types of sensors. The system consists of multiple nodes with microcontrollers of different architectures. One node is a control node and other nodes send data to it. The system is implemented and checked in the laboratory. For signal analysis, a digital oscilloscope and logic analyzer are used. Also, the obtained results and signal diagrams are presented as well.

Bearing Fault Diagnosis Based on Multisensor Information Coupling and Attentional Feature Fusion

The effective fault diagnosis of bearing can guarantee the safety of rotating machinery and is very important for its stable operation. The information fusion of multi-sensor data has been a feasible method to enhance the performance of fault diagnosis. However, how to fuse the joint information from different channels or even different kinds of sensors is still an important challenge. The present study proposes a novel multi-sensor information coupling network (MICN) for bearing fault diagnosis, which handles the signals from the same or different types of sensors, and the deeper features can be extracted from multi-sensors independently and simultaneously fused layer by layer. Especially, during the multi-layer feature fusion process, a novel feature-level information coupling model is developed based on the mutual attention mechanism. Finally, to validate the efficiency of the proposed method, several different experiments are designed, and the results show validity and superiority.

Method, Model and Information Technology for Determining the Value of Measurement Infor-mation in The Interests of Safety of Technical Objects

Problem. Extending the service life of technical objects is inextricably linked to the safety of their operation and impact on the environment. The decision should be made based on the analysis of information received from many sensors of different types. Goal. The goal is creating method, model and information technology for determining the value of measurement information in the interests of security of technical objects. Methodology. Solution of the set tasks involved application of the mathematical analysis, decision theory, metrology, theory of signal detection etc. Originality. The article presents a method of determining the value of measurement information for the safety of bridge structures, which is based on the theory of statistical solutions. Results. It has been proven that when developing information technologies on large bridges and other similar technical objects, it is necessary, on the one hand, to meet safety conditions, to minimize the average risk and, on the other hand, to choose information whose value exceeds a certain permissible threshold, which is different for bridge structures and depends on its equipment with sensors, the information technologies that will be used, and on many other factors that are characteristic of the bridge and its operating conditions. Practical value. The method, model and information technology developed for the bridge structure can be applied to other technical objects as well.

Blind Image Quality Assessment with Deep Learning: A Replicability Study and Its Reproducibility in Lifelogging

The wide availability and small size of different types of sensors have allowed for the acquisition of a huge amount of data about a person’s life in real time. With these data, usually denoted as lifelog data, we can analyze and understand personal experiences and behaviors. Most of the lifelog research has explored the use of visual data. However, a considerable amount of these images or videos are affected by different types of degradation or noise due to the non-controlled acquisition process. Image Quality Assessment can plays an essential role in lifelog research to deal with these data. We present in this paper a twofold study on the topic of blind image quality assessment. On the one hand, we explore the replication of the training process of a state-of-the-art deep learning model for blind image quality assessment in the wild. On the other hand, we present evidence that blind image quality assessment is an important pre-processing step to be further explored in the context of information retrieval in lifelogging applications. We consider that our efforts have been successful in the replication of the model training process, achieving similar results of inference when compared to the original version, while acknowledging a fair number of assumptions that we had to consider. Moreover, these assumptions motivated an extensive additional analysis that led to significant insights on the influence of both batch size and loss functions when training deep learning models in this context. We include preliminary results of the replicated model on a lifelogging dataset, as a potential reproducibility aspect to be considered.

Hand Gesture Recognition Using EMG-IMU Signals and Deep Q-Networks

Hand gesture recognition systems (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) have been studied for different applications in recent years. Most commonly, cutting-edge HGR methods are based on supervised machine learning methods. However, the potential benefits of reinforcement learning (RL) techniques have shown that these techniques could be a viable option for classifying EMGs. Methods based on RL have several advantages such as promising classification performance and online learning from experience. In this work, we developed an HGR system made up of the following stages: pre-processing, feature extraction, classification, and post-processing. For the classification stage, we built an RL-based agent capable of learning to classify and recognize eleven hand gestures-five static and six dynamic-using a deep Q-network (DQN) algorithm based on EMG and IMU information. The proposed system uses a feed-forward artificial neural network (ANN) for the representation of the agent policy. We carried out the same experiments with two different types of sensors to compare their performance, which are the Myo armband sensor and the G-force sensor. We performed experiments using training, validation, and test set distributions, and the results were evaluated for user-specific HGR models. The final accuracy results demonstrated that the best model was able to reach up to 97.50%±1.13% and 88.15%±2.84% for the classification and recognition, respectively, with regard to static gestures, and 98.95%±0.62% and 90.47%±4.57% for the classification and recognition, respectively, with regard to dynamic gestures with the Myo armband sensor. The results obtained in this work demonstrated that RL methods such as the DQN are capable of learning a policy from online experience to classify and recognize static and dynamic gestures using EMG and IMU signals.

Technological avenues and market mechanisms to accelerate methane and nitrous oxide emissions reductions.

Strategies targeting methane (CH4) and nitrous oxide (N2O) emissions are critical to meeting global climate targets. Existing literature estimates the emissions of these gases from specific sectors, but this knowledge must be synthesized to prioritize and incentivize CH4 and N2O mitigation. Accordingly, we review emissions sources and mitigation strategies in all key sectors (fuel extraction and combustion, landfilling, agriculture, wastewater treatment, and chemical industry) and the role of carbon markets in reducing emissions. The most accessible reduction opportunities are in the hydrocarbon extraction and waste sectors, where half (>3 Gt-CO2e/year) of the emissions in these sectors could be mitigated at no net cost. In total, 60% of CH4 emissions can be mitigated at less than $50/t-CO2. Expanding the scope of carbon markets to include these emissions could provide cost-effective decarbonization through 2050. We provide recommendations for carbon markets to improve emissions reductions and set prices to appropriately incentivize mitigation.

Shape-Former: Bridging CNN and Transformer via ShapeConv for multimodal image matching

As with any data fusion task, the front-end of the pipeline for image fusion, aiming to collect multitudinous physical properties from multimodal images taken by different types of sensors, requires registering the overlapped content of two images via image matching. In other words, the accuracy of image matching will influence directly the subsequent fusion results. In this work, we propose a hybrid correspondence learning architecture, termed as Shape-Former, which is capable of solving matching problems such as multimodal, and multiview cases. Existing attempts have trouble capturing intricate feature interactions for seeking good correspondence, if the image pairs simultaneously suffer from geometric and radiation distortion. To address this, our key is to take advantage of convolutional neural network (CNN) and Transformer for enhancing structure consensus representation ability. Specifically, we introduce a novel ShapeConv so that CNN and Transformer can be generalized to sparse matches learning. Furthermore, we provide a robust soft estimation of outliers mechanism for filtering the response of outliers before capturing shape features. Finally, we also propose coupling multiple consensus representations to further solve the context conflict problems such as local ambiguity. Experiments with variety of datasets reveal that our Shape-Former outperforms state-of-the-art on multimodal image matching, and shows promising generalization ability to different types of image deformations.

DPFL-Nets: Deep Pyramid Feature Learning Networks for Multiscale Change Detection.

Due to the complementary properties of different types of sensors, change detection between heterogeneous images receives increasing attention from researchers. However, change detection cannot be handled by directly comparing two heterogeneous images since they demonstrate different image appearances and statistics. In this article, we propose a deep pyramid feature learning network (DPFL-Net) for change detection, especially between heterogeneous images. DPFL-Net can learn a series of hierarchical features in an unsupervised fashion, containing both spatial details and multiscale contextual information. The learned pyramid features from two input images make unchanged pixels matched exactly and changed ones dissimilar and after transformed into the same space for each scale successively. We further propose fusion blocks to aggregate multiscale difference images (DIs), generating an enhanced DI with strong separability. Based on the enhanced DI, unchanged areas are predicted and used to train DPFL-Net in the next iteration. In this article, pyramid features and unchanged areas are updated alternately, leading to an unsupervised change detection method. In the feature transformation process, local consistency is introduced to constrain the learned pyramid features, modeling the correlations between the neighboring pixels and reducing the false alarms. Experimental results demonstrate that the proposed approach achieves superior or at least comparable results to the existing state-of-the-art change detection methods in both homogeneous and heterogeneous cases.

Vehicle Escape Mechanism

Abstract: The Idea is to make an Automated Vehicle Escape Mechanism or a Vehicle Escape device which would help the Passengers of the Car to get out in case of Certain Accidental Situations such as Drowning of Car and Car flipped Upside Down. Mechanism or a device used for breaking of window. Due to which person or people can be able to get out of car through window or windshield. In the case of drowning of car, window or glass which is not under water can be detected and breaks for getting out of car at the right time. The device is automatic as well as manual switch. Different types of sensors are involved for different situation scenarios.

Geostatistical Modelling of Soil Spatial Variability by Fusing Drone-Based Multispectral Data, Ground-Based Hyperspectral and Sample Data with Change of Support

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow for long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different types: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer, as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using Continuum Removal (CR) methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account the change of support. Secondly, the data acquired with the two different sensors were integrated with soil granulometric properties by using two multivariate geostatistical techniques: multi-collocated cokriging to achieve a more exhaustive and finer-scale soil characterization, and multi-collocated factor cokriging to extract synthetic scale-dependent indices (regionalized factors) for the delineation of soil in homogeneous zones. This paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalized factors at two different scales (two for each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterized by different granulometries and clay compositions. The applied method is sufficiently flexible and could be applied to any number and type of sensors.

Futuristic Advancements in Biomass‐Derived Graphene Nanoassemblies: Versatile Biosensors for Point‐of‐Care Devices

AbstractGraphene and its derivatives, graphene oxide (GO), and reduced graphene oxide (rGO) have gained a lot of interest in the field of biosensing applications due to their unique physicochemical properties. In recent days graphene derived from biomass waste has been undergoing a lot of development because it is stable, and has a structure with rich in carbon content, renewable and economically viable. In a way, it helps us in waste management and resource recovery. There are different ways to synthesize graphene and its derivatives through pyrolysis, chemical vapour deposition (CVD), and carbonization etc. In different types of sensors like electrochemical sensors, fluorescent sensors, surface plasmon resonance (SPR) sensors, and surface‐enhanced Raman scattering (SERS) sensors for detecting bacterial and viral pathogens, which can be designed from biomass derived graphene‐based nanostructures. These types of sensors can be used in Point of care (POC) and wearable devices, which are now witnessing a great deal of development. It helps us to carry on and analyze therapeutic and diagnoses for a disease from the comfort of our home which will be achieved soon.

Tool Wear Prediction Based on a Multi-Scale Convolutional Neural Network with Attention Fusion

Compared with traditional machine learning algorithms, the convolutional neural network (CNN) has an excellent automatic feature learning ability and can complete the nonlinear representation from original data input to output by itself. However, the CNN does not sufficiently mine the tool wear information contained in the multi-sensor data due to disregard of the differences in the contribution of different features when extracting features. In this paper, a tool wear prediction method based on a multi-scale convolutional neural network with attention fusion is proposed, which fuses the tool wear degradation information collected by different types of sensors. In the multi-scale convolution module, convolution kernels with different sizes are used to extract the degradation information of different scales in the wear information, and then the attention fusion module is constructed to fuse the multi-scale feature information. Finally, the mapping between tool wear and multi-sensor data is realized through the feature information obtained by residual connection and full connection layer. By comparing the multi-scale convolutional neural network with different attention mechanisms, the experiments demonstrated the effectiveness and superiority of the proposed method.

Subsystem With Ultrasonic and Passive Infrared Sensors for Pedestrian Detection

Despite the rapid development of technologies for the detection of pedestrians in traffic, there are still significant problems with automated vehicle management systems. Often these problems are caused by different external conditions, weather disasters, different light levels, day and night, and more. All these factors can affect the incorrect assessment of the system and the performed detection of the pedestrian object. Pedestrian fatalities have risen recently, even as vehicles are equipped with sophisticated avoidance technology. Vehicle-based sensors can fail to identify pedestrians especially when those pedestrians are small, too far or too close to the vehicle, or partially occluded by nearby objects. Pedestrians have variable physical characteristics and appear in a variety of environments with different background features. The paper analyzes the possibilities and problems that arise in the detection and recognition of pedestrians. Pedestrian safety is one of the important issues of traffic safety in urban areas. The application of various technologies in vehicles provides enormous opportunities to reduce accidents in which pedestrians are most often injured. The development of intelligent pedestrian recognition systems can be divided into two directions. Development of hardware part related to different types of sensors, and development of software part based on artificial neural networks and machine learning techniques. The paper analyzes various technologies used in pedestrian detection in automated vehicles with an emphasis on hardware development. A low-cost system based on two types of sensors, ultrasonic sensor and passive infrared, is proposed. Aspects of the application of such sensors with possibilities of implementation in complex computer vision systems are analyzed. The most common classes of sensors presently used in automated driving applications include visible-light cameras (VLC), light detection, and ranging (LiDAR).

Respirometric Techniques for Real-Time Monitoring of Corrosion Reactions

Recently, we have introduced novel respirometric methods for real-time monitoring of corrosion, both for atmospheric corrosion [1,2] and immersion conditions [3]. The approach is based on monitoring the rate of the cathodic reactions with help of different types of sensors; this enables to track the O2 reduction reaction and the H2 evolution reaction simultaneously. Different experimental setups have been developed, from static to flow cell scenarios. Moreover, combining respirometry with time-lapse microscopy reveals interesting details on initiation and propagation of corrosion. Most recently, the respirometric setups were coupled with electrochemistry [4], enlarging the corrosion cases to be studied from open-circuit conditions to potential-controlled systems. With this, it is possible for instance to follow and quantify the amount of oxygen evolving during transpassive dissolution of stainless steels at potentials above the water stability region (i.e., the net electrical current/charge can be assigned into partial currents for oxygen evolution and for metal dissolution). Examples will be presented, to illustrate the potential of these techniques to reveal new insights into corrosion mechanisms of active and passive metals and alloys.References M. Strebl, S. Virtanen, Real-Time Monitoring of Atmospheric Magnesium Alloy Corrosion, Journal of The Electrochemical Society 166 (2019) C3001-C3009M. Strebl, M. Bruns, S. Virtanen, Editors’ Choice—Respirometric in Situ Methods for Real-Time Monitoring of Corrosion Rates: Part I. Atmospheric Corrosion, J. Electrochem. Soc. 167 (2020) 21510M.G. Strebl, M.P. Bruns, G. Schulze, S. Virtanen, Respirometric In Situ Methods for Real-Time Monitoring of Corrosion Rates: Part II. Immersion, J. Electrochem. Soc. 168 (2021) 11502M.G. Strebl, M.P. Bruns, S. Virtanen, Coupling Respirometric HER and ORR Monitoring with Electrochemical Measurements, Electrochimica Acta 412 (2022) 140152

Algorithms for Flaw Detection by SHM with Static Sensors Approach

AbstractStructural Health Monitoring (SHM) is a technology aimed to monitor the soundness of the structures. Applications for aircraft structure are largely investigated. The goal is to utilize the information acquired during the monitoring to save maintenance costs, improve flight safety, and design lighter structures. There are different issues investigated by SHM research, like load monitoring and impact detection. The most primarily investigated issue is damage detection, that is, developing a system that utilizes sensors to detect the damages in the structure. The damage detection can be performed with two different types of sensors: dynamic and static sensors. Dynamic sensors work by the transmission of waves through the structures. Defects are identified by deviation and reflection of the waves by damages. The SHM approach described in this work uses static sensors, like strain gages or fiber optics. The strain fields under load in pristine and damaged conditions are compared; damages are identified by evaluating the change in the strain field. Developed and patented in Leonardo Aircraft, two different algorithms for damage detection are described in this work. The algorithms are based on reverse finite element model (F.E.M.) and neural networks.