Different Types Of Sensors Research Articles

Advancements in wearable heart sounds devices for the monitoring of cardiovascular diseases

AbstractCardiovascular diseases remain a leading global cause of mortality, underscoring the urgent need for intelligent diagnostic tools to enhance early detection, prediction, diagnosis, prevention, treatment, and recovery. This demand has spurred the advancement of wearable and flexible technologies, revolutionizing continuous, noninvasive, and remote heart sound (HS) monitoring—a vital avenue for assessing heart activity. The conventional stethoscope, used to listen to HSs, has limitations in terms of its physical structure, as it is inflexible and bulky, which restricts its prospective applications. Recently, mechanoacoustic sensors have made remarkable advancements, evolving from primitive forms to soft, flexible, and wearable designs. This article provides an in‐depth review of the latest scientific and technological advancements by addressing various topics, including different types of sensors, sensing materials, design principles, denoising techniques, and clinical applications of flexible and wearable HS sensors. This transformative potential lies in the capacity for ongoing, remote, and personalized monitoring, promising enhanced patient outcomes, amplified remote monitoring capabilities, and timely diagnoses. Last, the article highlights current challenges and prospects for the future, suggesting techniques to advance HS sensing technologies for exciting real‐time applications.

Overview of Sensor Applications in Intelligent Driving

With the rapid development of autonomous driving technology, sensors are the core component of the intelligent driving system, whose selection and combination are crucial to the safety and efficiency of the system. This paper reviews the applications of different types of sensors in intelligent driving systems, including cameras, LIDAR, millimeter-wave radar, and infrared sensors. By analyzing the best choice of these sensors in urban traffic, highway, night driving and field driving, this paper explores the effectiveness of the sensor combination and cites the relevant literature in the past five years.

Delay management for heterogeneous traffic in vehicular sensor networks using packet fragmentation of low priority data

AbstractVehicular sensor networks (VSNs) are expected to revolutionize the transportation systems through automated decision‐making. These networks function on the communication between vehicles, traffic infrastructure, and people. One of the major challenges of VSNs is to deal with heterogeneous traffic due to the diverse nature of information and data generated by different types of sensors and devices within the vehicular environment. To ensure an effective operation of the network, it is crucial to offer a differentiated quality of service to each traffic class. Media access control (MAC) protocols offer an opportunity to provide differentiated channel access to the traffic of different priorities. This work is focused on offering comparison of recent MAC schemes in terms of their performance for heterogeneous traffic generated by VSNs. Two protocols FROG‐MAC and UrgMAC have been selected: FROG‐MAC is developed using a simple concept of packet fragmentation, where the packets of lower priority are fragmented to offer an early transmission opportunity to urgent traffic; on the other hand, urgMAC is designed using advanced cross‐layer techniques of two‐tiered service differentiation mechanism, adaptive data rate adjustment mechanism, urgency‐based contention window size adaptation, traffic type adaptive duty cycle, and multimedia message passing. It has been found that the FROG‐MAC outperforms urgMAC in terms of delay, energy consumption and packet delivery ratio.

A review of deep learning-based stereo vision techniques for phenotype feature and behavioral analysis of fish in aquaculture

The industrialization, high-density, and greener aquaculture requires a more precise and intelligent aquaculture management. Phenotypic and behavioral information of fish, which can reflect fish growth and welfare status, play a crucial role in aquaculture management. Stereo vision technology, which simulates parallax perception of the human eye, can obtain the three-dimensional phenotypic characteristics and movement trajectories of fish through different types of sensors. It can overcome the limitations in dealing with fish deformation, frequent occlusions and understanding three-dimension scenes compared to the traditional two-dimensional computer vision techniques. With the deep learning development and application in aquaculture, stereo vision has become a super computer vision technology that can provide more precise and interpretable information for intelligent aquaculture management, such as size estimation, counting and behavioral analysis of fish. Hence, it is very beneficial for researchers, managers, and entrepreneurs to possess a thorough comprehension about the fast-developing stereo vision technology for modern aquaculture. This study provides a critical review of relevant topics, including the four-layer application structure of stereo vision technology in aquaculture, various deep learning-based technologies used, and specific application scenarios. The review contributes to research development by identifying the current challenges and provide valuable suggestions for future research directions. This review can serve as a useful resource for developing future studies and applications of stereo vision technology in smart aquaculture, focusing on phenotype feature extraction and behavioral analysis of fish.

A Multi-Active and Multi-Passive Sensor Fusion Algorithm for Multi-Target Tracking in Dense Group Clutter Environments

Multi-target tracking (MTT) of multi-active and multi-passive sensor (MAMPS) systems in dense group clutter environments is facing significant challenges in measurement fusion. Due to the difference in measurement information characteristics in MAMPS fusion, it is difficult to effectively correlate and fuse different types of sensors’ measurements, leading to difficulty in taking full advantage of various types of sensors to improve target tracking accuracy. To this end, we present a novel MAMPS fusion algorithm, which is based on centralized measurement association fusion (MAF) and distributed deep neural network (DNN) track fusion, named the MAMPS-MAF-DNN algorithm. Firstly, to reduce the impact of the dense group clutter, a clutter pre-processing algorithm is elaborated, which combines the advantages of the CFDP (cluster by finding density peaks) and double threshold screening algorithms. Then, for the single-active and multi-passive sensor (SAMPS) system, a centralized MAF algorithm based on angle information is developed, called the SAMPS-MAF algorithm. Finally, the SAMPS-MAF algorithm is extended to the MAMPS system within the DNN framework, and the complete MAMPS-MAF-DNN algorithm is proposed. Experimental results indicate that, compared to the existing MAF and covariance intersection (CI) fusion algorithms, the proposed MAMPS-MAF-DNN algorithm can fully combine the advantages of multi-active and multi-passive sensors, efficiently reduce the computational complexity, and obviously improve the tracking accuracy.

A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment.

Floods are the most destructive, widespread, and frequent natural hazards. The extent of flood events is accelerating in the context of climate change, where flood management and disaster mitigation remain important long-term issues. Different studies have been utilizing data and images from various types of sensors for mapping, assessment, forecasting, early warning, rescue, and other disaster prevention and mitigation activities before, during, and after floods, including flash floods, coastal floods, and urban floods. These monitoring processes evolved from early ground-based observations relying on in situ sensors to high-precision, high-resolution, and high-coverage monitoring by airborne and remote sensing sensors. In this study, we have analyzed the different kinds of sensors from the literature review, case studies, and other methods to explore the development history of flood sensors and the driving role of floods in different countries. It is found that there is a trend towards the integration of flood sensors with artificial intelligence, and their state-of-the-art determines the effectiveness of local flood management to a large extent. This study helps to improve the efficiency of flood monitoring advancement and flood responses as it explores the different types of sensors and their effectiveness.

A NOVEL APPROACH OF HUMAN EMOTIONAL ESTIMATION SYSTEM VIA MULTISENSORY DATASETS FUSION METHODS

Human emotional understanding has become one of the most rapidly emerging fields developed over the years since the emotional state is created and influenced by several neither easily measured and nor predicted factors. The proposed novel approach is based on the mixture of the processed acceleration data of several designed activities and processed heart rate data under several emotional states, recorded by the application of`smart band devices. Since the processed acceleration and heart rate data are running on different dimensionalities and recorded by different types of sensors, some machine learning-based methods applied to fusing these two kinds of data. The results of the data fusion methods are defined as the new fused datasets for Multi-Layer Perceptron and Support Vector Machines classifiers based on a selected criterion. At last, the accuracy results of the selected classifiers running under the defined machine-learning-based fusion dataset compared to each other to find out the effectiveness of the proposed fusion method.

An Accurate Classification for Human Sport Activity Recognition from Sensor Data Using Hybrid Machine Learning Models

The significance of human sports activities recognition (HSAR) in many computing applications, including sports analytics, smart surroundings, and healthcare monitoring is huge. The current use of vision sensors for HSAR is a difficult task due to the intricate movements involved in sports and fitness workouts, as well as fluctuations in illumination conditions. Therefore, a comprehensive review of machine learning algorithms (such as XGBoost, Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbors (KNN)) and their usage for human sports activities classification based on sensor data called ML-HSAR is presented in this article. A dataset containing 19 different human activities gathered from 8 different people through different types of sensors was used for this work; the dataset contains activities like sitting and standing, as well as other activities that require serious movements, such as running, cycling, and playing basketball. This study primarily aims to determine the performance of the considered ML models in accurately classifying these activities found in the dataset. The training of the algorithms and the subsequent evaluation were done on the segmented sensor data, effectively leveraging the dataset’s temporal aspect. In addition to the presented results for each of these algorithms, the limitations of each algorithm and their strengths in handling the complexities involved in HSAR were also presented. The contribution of this analysis is towards the understanding of the most appropriate ML models for HSAR tasks; it also offers valuable insights for future research and practical applications.

Ai-enhanced thermal modeling for integrated process-product-system optimization in zero-defect manufacturing chains

This paper proposes a novel approach for realising zero-defect manufacturing by integrating AI-based thermal modelling to perform multi-stage process, product, and system optimisation in complex manufacturing chains. It integrates advanced thermal simulation and sensor data feed in real-time as a multistage thermal prediction and management system via machine learning algorithms. This framework seeks to predict thermal behaviour using a combination of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and graph neural networks (GNNs) for encoding, predicting, and learning the spatial, temporal, and interactive thermal behaviour, respectively. It further embeds finite element analysis (FEA) simulations for high-fidelity thermal predictions using data fusion through Kalman filters. This helps obtain the optimal estimates of thermal states from sensor measurements involving different types of sensors and the characteristics of signals. A multistage multimodal optimization framework involves genetic algorithms (GA) for global thermal parameter optimisation, reinforcement learning (RL) for multi-stage dynamic process control optimisation, and multi-agent systems (MAS) for coordinated multi-stage multi-objective balance embedded in a digital twin architecture. Evaluation results show that the effectiveness of the proposed system in improving the overall production efficiency is 33%, reducing defects is 47%, and reducing energy utilisation is 22%, when compared to the current de facto approaches. There is also a 38% improvement in predictive capability in preventing, detecting, and predicting of cross-stage process faults.

Review and analysis of software simulators for robotic information systems

The rapid development of educational technologies requires the active involvement of new means and effective teaching methods. One of the innovative tools of learning in the field of robotics is the use of robot simulators in the educational process, which open up many opportunities for better assimilation of knowledge and development of practical skills among students. Robot simulators are software complexes that allow you to visualize and simulate the behavior of real robots. With these tools, students can: program robots in a virtual environment, test code and debug without the risk of damaging real equipment; to study robotics and the principles of robot operation, exploring different types of sensors, actuators and algorithms of robot behavior; to solve problems in various fields, such as production automation, logistics, construction, etc.; practices your skills in a safe and controlled environment. Among the benefits of using robotic simulators are next: availability for a large number of students, as simulators are much cheaper than real jobs; safety, as the virtual environment eliminates the risks associated with working with real robots; flexibility that allows students to work at their own pace, explore different scenarios and experiment with different parameters; interactivity with a realistic 3D-environment and visualization. This work presents an overview and analysis of software that allows modeling robot control systems and simulating their operation for various algorithms. A review and analysis of the proposed software tools for simulating robotic systems, which are used to teach bachelor's and master's degree students in the specialties of "Computer Science", "Information Systems", "Industrial Mechanical Engineering" and "Applied Mechanics" and have proven their effectiveness and effectiveness in distance learning at the Kyiv National University of Construction and Architecture.

Soft Multimodal Sensors with Decoupled Multimodality and Minimal Wiring for Wearable Systems

AbstractSoft multimodal sensors represent a promising approach for applications in wearable systems and skin‐adhesive devices, benefiting from their flexibility and body compatibility. However, the complexity of sensor wiring design poses challenges while employing multimodal decoupling. Although recent studies have applied various methods, such as adjusting the electrical properties of materials, these methods encounter limitations in signal decoupling when multichannel measurements are implemented. In this study, a signal measurement method utilizing frequency response analysis is developed using a multi‐band band‐stop filter (MBBSF) for multiple multimodal sensors. This approach enables the decoupling of different types of sensors and allows for the simultaneous collection of sensor data through a two‐wire connection. Consequently, it facilitates the concurrent and independent real‐time assessment of strain and force in each sensor. A sensor glove is developed incorporating multiple multimodal sensors with a two‐wire connection to measure grip posture and force. This glove is used effectively to classify unknown objects, thereby demonstrating its practical utility. This practical application provides a promising solution to the complex wiring challenges commonly encountered in multi‐modal sensor wearable systems.

Feasibility Study and Results from a Baseline Multi-Tool Active Seismic Acquisition for CO2 Monitoring at the Hellisheiði Geothermal Field

CO2 capture and underground storage, combined with geothermal resource exploitation, are vital for future sustainable and renewable energy. The SUCCEED project explores the feasibility of re-injecting CO2 into geothermal fields to enhance production and store CO2 for climate change mitigation. This integration requires novel time-lapse monitoring approaches. At the Hellisheiði geothermal power plant in Iceland, seismic surveys utilizing conventional geophones and a permanent fiber-optic helically wound cable (HWC) for Distributed Acoustic Sensing (DAS) were designed to provide subsurface information and CO2 monitoring. This work details the feasibility study and active seismic acquisition of the baseline survey, focusing on optical fiber sensitivity, seismic modeling, acquisition parameters, source configurations, and quality control. Post-acquisition signal analysis using a novel electromagnetic vibrating source is discussed. The integrated analysis of datasets from co-located sensors improved quality-control performance and geophysical interpretation. The study demonstrates the advantages of using densely sampled DAS data in space by multichannel processing. This experimental work highlights the feasibility of using HWC DAS cables in active surface seismic surveys with an environmentally friendly electromagnetic source, providing also a unique case of joint signal analysis from different types of sensors in high-temperature geothermal areas for energy and CO2 storage monitoring in a time-lapse perspective.

Thin Film Semiconductor Metal Oxide Oxygen Sensors: Limitations, Challenges, and Future Progress

Among oxygen sensors, types such as polymer-, ceramic-, or carbon-based ones may be distinguished. Particular interest in semiconductor metal oxide (SMO) sensors has recently been observed. This is due to their easy fabrication process, high control over the final product (dopants, posttreatment, etc.), and high concentration of oxygen vacancies, by which they show significant changes in electrical properties when exposed to analyte. In this review, different types of sensors are described and categorized. Importantly, their limitations, challenges and principles of sensing mechanism are also discussed, wherein attention is primarily paid to semiconductor metal oxide (SMO) oxygen sensors. This comprehensive review provides an in-depth analysis of the existing literature on planar SMO oxygen sensors, focusing on various materials, fabrication techniques, and sensing mechanisms. It also critically assesses the challenges and limitations in current research, offering insights into future directions for developing highly efficient and reliable sensors. Currently, most oxygen resistive sensors are a few micrometers thick and operate at high temperatures, which leads to high power consumption. To highlight importance of this topic, a market overview is also presented.

On the Reliability of Wireless Sensor Networks with Multiple Sinks.

The convergence of heterogeneous wireless sensor networks provides many benefits, including increased coverage, flexible load balancing capabilities, more efficient use of network resources, and the provision of additional data by different types of sensors, thus leading to improved customer service based on more complete information. However, despite these advances, the challenge of ensuring reliability and survivability remains due to low-cost sensor requirements and the inherent unreliability of the wireless environment. Integrating different sensor networks and unifying protocols naturally leads to the creation of a network with multiple sinks, necessitating the exploration of new approaches to rational reliability assurance. The failure of some sensors does not necessarily lead to a shutdown of the network, since other sensors can duplicate information and deliver data to sinks via an increased number of alternative routes. In this paper, the reliability indicator is defined as the probability that sinks can collect data from a given number of sensors. In this context, a dedicated reliability metric is introduced and examined for its effectiveness. This metric is computed using an algorithm rooted in the modified factoring method. Furthermore, we introduce a heuristic algorithm designed for optimal sink placement in wireless sensor networks to achieve the highest level of network reliability.

Implantable and Semi-Implantable Biosensors for Minimally Invasive Disease Diagnosis

Implantable and semi-implantable biosensors fabricated with biodegradable materials and nanomaterials have gained interest in the past few decades. Functionalized biodegradable materials and nanomaterials are usually employed to satisfy clinical and research requirements because of their advanced properties. Novel fabrication techniques were developed to improve the efficiency and accuracy. Different working mechanisms were facilitated to design different types of sensors. This review discusses the recent developments of implantable and semi-implantable biosensors. The materials and fabrications are browsed, and different types of biomedical sensors for different variables are discussed as a focused topic. The biomedical sensors are discussed according to the targets and working mechanisms, followed by a focus on the nervous system sensing to provide an inspiration that different variables can be studied simultaneously on the single system. In the end, challenges and prospects will be discussed. This review aims to provide information of materials, fabrication approaches, mechanisms, and the state of the art for inspiration in designing novel implantable and semi-implantable biomedical sensors for general diagnostic activities.

The Diversity of MOF Structures and Their Impact on Photoelectrochemical Sensors for Monitoring Environmental Pollution

The problem of environmental pollution is one of the most important in the modern world. Pollution causes an increase in human diseases, the extinction of many species of plants and animals, global warming, and many weather anomalies. One of the great challenges for scientists is the development of methods for monitoring and removing the emerging pollutants. This review focuses on Metal–Organic Frameworks (MOFs) and their use as working material to construct different types of sensors for application in environmental pollution monitoring. In particular, the detection of heavy metals (mercury, lead, and arsenic) and organic compounds (drugs, biomolecules, and pesticides) are considered. The collected data show that photoelectrochemical (PEC) sensors based on MOFs are the most fascinating materials due to various combinations (e.g., surface modification) and operational possibilities. PEC sensors achieve enormous sensitivity, which increases even to the pico level, making it the best tool in sensing applications. This review also highlights the main sensor challenges. Most of them are concerned with the possibility of reusing the sensor, its regeneration, and safe disposal. In addition, more attention should be paid to the sensor manufacturing process, which often uses toxic compounds, and research to eliminate them in favor of non-toxic compounds.

Advances in adsorption technologies for hexavalent chromium removal: Mechanisms, materials, and optimization strategies

In the realm of environmental engineering and water treatment, significant attention has been devoted to the removal of hexavalent chromium Cr (VI) from wastewater. Delving into the mechanisms of adsorption reveals a complex interplay of ion exchange, surface complexation, and electrostatic interactions dictating the binding of Cr (VI) to various adsorption materials. Utilizing diverse adsorbents like 2-D materials, metal oxides, and bio-based substances is driven by their exceptional absorptive capacity and specificity. Key parameters such as pH, temperature, initial concentration, and dosage are scrutinized to optimize the efficiency of adsorbent removal and ascertain the absorptive nature of the adsorbate. Beyond natural sources, human activities have exacerbated chromium contamination across ecosystems, posing a threat to food chains due to chromium's non-biodegradable nature and persistence. Chromium affects a wide range of life forms, including humans, aquatic organisms, and terrestrial species, through the accumulation and repercussions of chromium discharge. The leaching of chromium into ground and surface water triggers various health issues such as dermatitis, allergic reactions, skin ulcerations, bronchial carcinoma, asthma, and gastroenteritis. This review intended to list different treatment technologies for chromium removal along with their mechanism. Different types of sensors used for chromium detection have also been explored.

Consequences of thermal aging on the detection of impact damage in composite structures with PZT/FBG guided wave systems

In the context of recent reusable launch vehicles (RLV) developments, SHM could help fast and economic revalidation of RLVs between launches, provided that the transducers keep their functionality and reliability after repeating launches being exposed to harsh flight conditions. This paper studies the detection of barely visible impact damage in composite plates with guided waves (GW) based SHM systems. More precisely, it focuses on how this detection can be affected by the degradation of the sensors potentially induced by the exposition to flight conditions. The aging conditions in this work mainly consist of thermal cycling with high maximum temperature (150°C), expected for RLV structures and close to the operational limit of the thermoset-matrix composite. The response of the sensors to such environmental conditions depends on the sensor itself and on its bonding to the structure. Thus, in this paper two different types of sensors are tested (commonly used PZT transducers and FBG sensors) along with two different bonding methods (classic bonding with a secondary adhesive and cobonding on composite surface during the curing process). The sensors are setup in pairs (PZT-PZT or PZT-FBG) with GW signal paths going through the impact location. After a baseline acquisition, incremental impact tests (with increasing energy) are performed and GW signals are acquired after each impact to assess the damage signature on the waveform. Then after submitting the plate to thermal cycling, GW signals are recorded again to assess its effect on the damage detection performance and on the risk of false alarms (mistaking sensor degradation for structural damage). Sensor degradations are monitored through non-destructive techniques and impedance self-diagnostic measurements for PZT. Additionally to this aging test, the responses of degraded sensors previously studied by the authors are compared with the impact signature to assess how those degraded sensors could harm the impact detection.

Damage detection using different types of sensors

Damage detection using different types of sensors

Optimal Design of Data Acquisition System for Fault Diagnosis of Hydraulic Machinery

Abstract Fault diagnosis of hydraulic machinery has high requirements on the synchronization of monitoring data and the anti-interference of monitoring equipment. The performance and data quality of the existing operation monitoring equipment of the unit cannot meet the requirements. At the same time, due to the variety of sensors and the different wiring methods of different types of sensors, the preparation work such as wiring of data acquisition equipment in the test is complicated. This paper analyzes the application requirements of fault diagnosis data and the problems existing in the conventional data collection process. A data collection solution specified for diagnosis of hydraulic machinery, which better solves the problem of data synchronization, is designed. The new designed system has high sampling accuracy and anti-interference characteristics, as well as the advantages of strong capability and simple equipment deployment. The proposed system has been applied in multiple power stations, pumping stations and pump test platforms. The relevant functions have been effectively verified.