Journal articles: 'Intelligent sensors'

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Fu, Jie, Qiya Gao, and Shuang Li. "Application of Intelligent Medical Sensing Technology." Biosensors 13, no. 8 (August 13, 2023): 812. http://dx.doi.org/10.3390/bios13080812.

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With the popularization of intelligent sensing and the improvement of modern medical technology, intelligent medical sensing technology has emerged as the times require. This technology combines basic disciplines such as physics, mathematics, and materials with modern technologies such as semiconductors, integrated circuits, and artificial intelligence, and has become one of the most promising in the medical field. The core of intelligent medical sensor technology is to make existing medical sensors intelligent, portable, and wearable with full consideration of ergonomics and sensor power consumption issues in order to conform to the current trends in cloud medicine, personalized medicine, and health monitoring. With the development of automation and intelligence in measurement and control systems, it is required that sensors have high accuracy, reliability, and stability, as well as certain data processing capabilities, self-checking, self-calibration, and self-compensation, while traditional medical sensors cannot meet such requirements. In addition, to manufacture high-performance sensors, it is also difficult to improve the material process alone, and it is necessary to combine computer technology with sensor technology to make up for its performance shortcomings. Intelligent medical sensing technology combines medical sensors with microprocessors to produce powerful intelligent medical sensors. Based on the original sensor functions, intelligent medical sensors also have functions such as self-compensation, self-calibration, self-diagnosis, numerical processing, two-way communication, information storage, and digital output. This review focuses on the application of intelligent medical sensing technology in biomedical sensing detection from three aspects: physical sensor, chemical sensor, and biosensor.

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Taymanov, Roald E., and Ksenia Sapozhnikova. "Problems of Terminology in the Field of Intelligent Sensors and Systems." Key Engineering Materials 437 (May 2010): 434–38. http://dx.doi.org/10.4028/www.scientific.net/kem.437.434.

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Terms and definitions in the field of sensors and measuring systems with the elements of artificial intelligence are not well-established. Nowadays, in Russia a draft of state standard “Intelligent Sensors and Intelligent Measuring Systems. Basic Terms and Definitions” is being under development. In accordance with the draft of the standard, the paper deals both with definitions and grounds of a number of terms such as “adaptive sensor”, “metrological self-check of sensors”, “metrological diagnostic check”, “intelligent sensor”, etc.

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Micko, Kristian, Peter Papcun, and Iveta Zolotova. "Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure." Sensors 23, no. 9 (May 4, 2023): 4469. http://dx.doi.org/10.3390/s23094469.

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An intelligent transportation system is one of the fundamental goals of the smart city concept. The Internet of Things (IoT) concept is a basic instrument to digitalize and automatize the process in the intelligent transportation system. Digitalization via the IoT concept enables the automatic collection of data usable for management in the transportation system. The IoT concept includes a system of sensors, actuators, control units and computational distribution among the edge, fog and cloud layers. The study proposes a taxonomy of sensors used for monitoring tasks based on motion detection and object tracking in intelligent transportation system tasks. The sensor’s taxonomy helps to categorize the sensors based on working principles, installation or maintenance methods and other categories. The sensor’s categorization enables us to compare the effectiveness of each sensor’s system. Monitoring tasks are analyzed, categorized, and solved in intelligent transportation systems based on a literature review and focusing on motion detection and object tracking methods. A literature survey of sensor systems used for monitoring tasks in the intelligent transportation system was performed according to sensor and monitoring task categorization. In this review, we analyzed the achieved results to measure, sense, or classify events in intelligent transportation system monitoring tasks. The review conclusions were used to propose an architecture of the universal sensor system for common monitoring tasks based on motion detection and object tracking methods in intelligent transportation tasks. The proposed architecture was built and tested for the first experimental results in the case study scenario. Finally, we propose methods that could significantly improve the results in the following research.

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Vladareanu, Luige. "Advanced Intelligent Control through Versatile Intelligent Portable Platforms." Sensors 20, no. 13 (June 29, 2020): 3644. http://dx.doi.org/10.3390/s20133644.

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Deep research and communicating new trends in the design, control and applications of the real time control of intelligent sensors systems using advanced intelligent control methods and techniques is the main purpose of this research. The innovative multi-sensor fusion techniques, integrated through the Versatile Intelligent Portable (VIP) platforms are developed, combined with computer vision, virtual and augmented reality (VR&AR) and intelligent communication, including remote control, adaptive sensor networks, human-robot (H2R) interaction systems and machine-to-machine (M2M) interfaces. Intelligent decision support systems (IDSS), including remote sensing, and their integration with DSS, GA-based DSS, fuzzy sets DSS, rough sets-based DSS, intelligent agent-assisted DSS, process mining integration into decision support, adaptive DSS, computer vision based DSS, sensory and robotic DSS, are highlighted in the field of advanced intelligent control.

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Jiang, Shali, and Qiong Ren. "Application of Sensors in Intelligent Clothing Design." International Journal of Online Engineering (iJOE) 14, no. 06 (June 22, 2018): 4. http://dx.doi.org/10.3991/ijoe.v14i06.8694.

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<p class="0abstract"><span lang="EN-US">In order to study the application of sensors in intelligent clothing design, the artificially intelligent cutting-edge technology -machine learning method was proposed to combine a variety of signals of non-contact sensors in several different positions. Higher accuracy was achieved, while maintaining the comfort brought by a non-contact sensor. The experimental results showed that the proposed strategy focused on the combination of clothing design technology and artificial intelligence technology. As a result, without changing the sensor materials, it enhances the comfort and precision of clothing, eliminates the comfort reduced by sensor close to the skin, and transforms inaccurate measurement into accurate measurement. </span></p>

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White, Neil. "Intelligent sensors." Sensor Review 17, no. 2 (June 1997): 97–98. http://dx.doi.org/10.1108/02602289710170267.

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Hashikawa, Fumitaka, and Kazuyuki Morioka. "Convenient Position Estimation of Distributed Sensors in Intelligent Spaces Using SLAM for Mobile Robots." Journal of Robotics and Mechatronics 27, no. 2 (April 20, 2015): 191–99. http://dx.doi.org/10.20965/jrm.2015.p0191.

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<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270002/09.jpg"" width=""200"" />Overview of the proposed method</div> Intelligent space is one in which many networked sensors are distributed. The purpose of intelligent space is to support information for human beings and robots based on the integration of sensor information. Specifically, to support location-based applications in intelligent space, networked sensors must get locations of human beings or robots. To do so, sensor locations and orientations of sensors must be known in world coordinates. To measure numerous sensor locations accurately by hand, this study focuses on estimating the locations and orientations of distributed sensors in intelligent space – but doing so automatically. We propose map sharing using distributed laser range sensors and a mobile robot to estimate the locations of distributed sensors. Comparing maps of sensor and robots, sensor locations are estimated on a global map built by SLAM of a mobile robot. An ICP matching algorithm is used to improve map matching among sensors and robots. Experimental results with actual distributed sensors and a mobile robot show that the proposed system estimates sensor locations satisfactorily and improve the accuracy of a global map built by SLAM. </span>

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Gao, Qiya, Jie Fu, Shuang Li, and Dong Ming. "Applications of Transistor-Based Biochemical Sensors." Biosensors 13, no. 4 (April 11, 2023): 469. http://dx.doi.org/10.3390/bios13040469.

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Transistor-based biochemical sensors feature easy integration with electronic circuits and non-invasive real-time detection. They have been widely used in intelligent wearable devices, electronic skins, and biological analyses and have shown broad application prospects in intelligent medical detection. Field-effect transistor (FET) sensors have high sensitivity, reasonable specificity, rapid response, and portability and provide unique signal amplification during biochemical detection. Organic field-effect transistor (OFET) sensors are lightweight, flexible, foldable, and biocompatible with wearable devices. Organic electrochemical transistor (OECT) sensors convert biological signals in body fluids into electrical signals for artificial intelligence analysis. In addition to biochemical markers in body fluids, electrophysiology indicators such as electrocardiogram (ECG) signals and body temperature can also cause changes in the current or voltage of transistor-based biochemical sensors. When modified with sensitive substances, sensors can detect specific analytes, improve sensitivity, broaden the detection range, and reduce the limit of detection (LoD). In this review, we introduce three kinds of transistor-based biochemical sensors: FET, OFET, and OECT. We also discuss the fabrication processes for transistor sources, drains, and gates. Furthermore, we demonstrated three sensor types for body fluid biomarkers, electrophysiology signals, and development trends. Transistor-based biochemical sensors exhibit excellent potential in multi-mode intelligent analysis and are good candidates for the next generation of intelligent point-of-care testing (iPOCT).

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Nowaková, Jana, and Miroslav Pokorný. "Intelligent Controller Design by the Artificial Intelligence Methods." Sensors 20, no. 16 (August 10, 2020): 4454. http://dx.doi.org/10.3390/s20164454.

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With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the states and processes. Standard controllers are available and successfully implemented. However, with the data-driven era we are facing nowadays, there is an opportunity to use controllers, which can include much information, elusive for common controllers. Our goal is to propose a design of an intelligent controller–a conventional controller, but with a non-conventional method of designing its parameters using approaches of artificial intelligence combining fuzzy and genetics methods. Intelligent adaptation of parameters of the control system is performed using data from the sensors measured in the controlled process. All parts designed are based on non-conventional methods and are verified by simulations. The identification of the system’s parameters is based on parameter optimization by means of its difference equation using genetic algorithms. The continuous monitoring of the quality control process and the design of the controller parameters are conducted using a fuzzy expert system of the Mamdani type, or the Takagi–Sugeno type. The concept of the intelligent control system is open and easily expandable.

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Wu, Guang, and Xuezheng Zhang. "Realization of Wireless Sensors and Intelligent Computer Aided Teaching in Physical Education and Training." Wireless Communications and Mobile Computing 2022 (February 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/6415352.

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Wireless sensors integrate a variety of high-tech, powered by engines to form network nodes, and randomly distributed sensors and data processing units form a network through a certain rule. The wireless sensor can directly transmit the collected digital signal to the computer for analysis and processing. Intelligent computer refers to a computer that can simulate and expand artificial intelligence. It is a dynamically developing concept and always represents the most advanced level of computer development. This article aims to study the role of wireless sensors and intelligent computer-assisted teaching in physical education and training. It is hoped that wireless sensors and intelligent computer technology will assist in physical training. This article briefly summarizes the status quo of the use of wireless sensors, briefly introduces the design and use of various wireless sensors, and briefly explains their differences. This article explains the theoretical basis and principles of use of intelligent computers. Aiming at the shortcomings of traditional multimedia courseware in the teaching process, an application idea is proposed, and a modular design method is adopted to design a computer-assisted teaching system based on integrated ware. The test results of this paper show that 40% of the students were very satisfied with the intelligent PC-assisted educational delivery system in PE lessons, 67% were satisfied with the usage of the intelligent CC-AS in PE lessons and 22% disagreed with the usage of the intelligent CC-AS in PE lessons; 14.2% of the teachers often used the intelligent CC-AS in PE lessons and 30% of teachers occasionally used ICDS in PE lessons and another 12% never used ICDS in PE lessons.

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Wang, Gai Fang, Feng Feng Fan, Xi Tao Xing, and Yong Wang. "Design and Implementation of Digital Sensor Simulator." Applied Mechanics and Materials 411-414 (September 2013): 1581–87. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1581.

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With the rapid development of sensor technology recently, sensors have been applied to various fields for detecting object states, e.g. intelligent agriculture, intelligent power, intelligent city, the Internet of Things, etc., and have becoming more and more critical for dynamic data acquisition. Due to detection environment, detection technology, costs and other factors, access to actual sensors for developing or debugging a sensor application may cause additional costs and time. Meanwhile, testing new sensor applications and protocols needs appropriate feasible ways with low costs and short time. Therefore, it is fairly urgent for designing and developing a simulation environment of sensors and sensor applications. This paper parsed the general structure of digital sensors, and then designed domain-based high level architecture of digital sensor simulator. Finally, the prototype of digital sensor simulator was developed and demonstrated the proper performance. Results show that digital sensor simulator would provide an effective way for testing novel sensors and protocols and also play an important role for constituting sensor network simulation environment.

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Pan, Chaolin. "Research on cross-field applications and future prospects of intelligent sensors." Applied and Computational Engineering 41, no. 1 (February 22, 2024): 210–14. http://dx.doi.org/10.54254/2755-2721/41/20230749.

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The Internet of Things (IoT) has transformed daily life through enhanced internet and communication technologies. Intelligent sensors, like temperature and humidity sensors, have revolutionized smart homes, enabling remote control and enhancing comfort. In agriculture, these sensors monitor soil moisture and crop conditions, optimizing farming practices for increased yields and sustainability. Healthcare benefits from the IoT with sensors in medical devices and wearables, offering real-time patient monitoring and cost reduction. Logistics employs intelligent sensors to track goods, boosting supply chain efficiency. Projects like IoT-based road analysis systems and pest monitoring exemplify data collection and intelligent decision-making, benefiting urban management and agriculture. It can be seen that intelligent sensors have an irreplaceable role in the Internet of Things, so this paper will introduce the technology and working principles of intelligent sensors and discuss the cross-domain applications and future expectations of intelligent sensors. Through literature analysis, it can be concluded that after the intelligent sensor receives the external signal, it will convert the signal into a physical signal related to the measured parameters and process, store, and communicate it so as to realize the detection, control, and data transmission of the environment. This means that intelligent sensors can be widely used in industrial production, environmental monitoring, medical health, and other fields.

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Ohtake, Hiroshi, and Toshihide Watanabe. "CMOS Image Sensor Dreams of Intelligent Sensors. Amplified Image Sensors." Journal of the Institute of Image Information and Television Engineers 53, no. 2 (1999): 158–65. http://dx.doi.org/10.3169/itej.53.158.

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Bialas, Andrzej. "Intelligent Sensors Security." Sensors 10, no. 1 (January 22, 2010): 822–59. http://dx.doi.org/10.3390/s100100822.

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Tomažič, Simon. "Intelligent Soft Sensors." Sensors 23, no. 15 (August 3, 2023): 6895. http://dx.doi.org/10.3390/s23156895.

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In this Special Issue, we embark on a journey into the exciting field of intelligent soft sensors, and take a deep dive into the groundbreaking advances and potential that these software algorithms have introduced in various fields [...]

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Duan, Hongyi, and Niucong Liu. "Research on New Hotel Intelligent Non-inductive Power Supply System." Journal of Physics: Conference Series 2562, no. 1 (August 1, 2023): 012039. http://dx.doi.org/10.1088/1742-6596/2562/1/012039.

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Abstract The mainstream hotel intelligent sensorless power acquisition system in the market is mainly composed of infrared sensors and wireless timing switches, among which the infrared sensor is the core device. The widely used infrared sensors in the market generally detect the temperature changes that occur when a person moves in a room to determine whether there is someone in the room. This means that when a person stays stationary for a long time, the machine may not be able to recognize the temperature changes, resulting in problems such as power outages in the room. Therefore, the existing products have some shortcomings and still need to be optimized. Therefore, in response to the problem that infrared sensors cannot respond to a stationary human body for a long time, a device capable of periodically shielding the probe of an internal pyroelectric sensor is studied in the product structure to enhance the sensor’s sensitivity to a stationary human body, thereby solving the problem of not responding to a stationary human body for a long time.

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SHIMOJO, MAKOTO. "A lecture on intelligent sensors.(5).Intelligent sensors for robots." Journal of the Fuel Society of Japan 70, no. 6 (1991): 554–59. http://dx.doi.org/10.3775/jie.70.554.

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Kryuk, Roman, Marina Kurbanova, Anastasia Kolbina, Konstantin Plotnikov, Igor Plotnikov, Andrey Petrov, and Mohammed El Amine Khelef. "Color Sensors “In Intelligent Food Packaging”." Food Processing: Techniques and Technology 52, no. 2 (July 6, 2022): 321–33. http://dx.doi.org/10.21603/2074-9414-2022-2-2366.

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The quality of food products depends not only on the technological parameters of production process, but also on storage conditions. Smart packaging controls storage conditions and tracks violations. The research objective was to review the use of sensors in food storage. The study featured publically available information on sensor-equipped smart food packaging. The information was obtained from open sources published in 2016–2021. The paper describes biosensors, chemical sensors, and indicators that determine some of the characteristics of food products and provide information to the producer, retailer, and consumer. Indicators proved to be the most promising type of sensors used in smart packaging. This type of sensor indicates the presence and concentration of various analytes through color change. The best indicators are those based on radio frequency (RFID), poison identifiers, compaction/leaks indicators, indicators of freshness/ripeness, etc. All the considered indicators visualize their data by changing color, which makes it possible to assess the quality of food products on the market. Sensor-based smart packaging is a promising direction in food industry because they make it possible to monitor and control product quality. Smart packaging allows consumers to check the freshness of products by themselves.

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Reddy, Vijaya Bhasker, Balk Dinesh, B. Manikyam, Gayatri, Shravan Kumar, P. Surekha, and Sami Anand. "Home Automation using Artificial Intelligent & Internet of Things." MATEC Web of Conferences 392 (2024): 01058. http://dx.doi.org/10.1051/matecconf/202439201058.

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Nowadays home automation is based on the automation process or else the Internet of Things. This paper presents combination of artificial intelligence and the Internet of things-based automation system. Here we develop some fundamental algorithms of artificial intelligence and then integrate them with electronic sensors. The main part of the Internet of Things is a Controller, we are using an Arduino based controller module here. All the sensors used in home automation are in turn connect to this Arduino controller. These Sensors will read the data based on the specifications, then that data will send to the microcontroller to analyze and process and later the Artificial Intelligence starts working as per the data received. Our home appliances start acting depending on the sensor's values obtained. For suppose, if the climate is too cool it will automatically switch OFF the FAN (or) AC. Using AI based face recognition system will operate the doors to close and open, also it can give alerts. By using a water level sensor, we will come to know the water level of the tank, with the help of this ta the motor pump will be ON or OFF according to the values of the water level sensor. And using an MQ-08 sensor the smoke value will be measured in the house, if the smoke value will be HIGH, it may give an alert.

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Takahashi, Kiyoshi, and Shinji Nozaki. "From intelligent sensors to fuzzy sensors." Sensors and Actuators A: Physical 40, no. 2 (February 1994): 89–91. http://dx.doi.org/10.1016/0924-4247(94)85010-0.

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Tian, Zuo Xi, Z. Dong, G. Y. Yang, and C. J. Chen. "Multi-Sensor Network Design Based on Intelligent Node." Applied Mechanics and Materials 50-51 (February 2011): 992–97. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.992.

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To resolve the problem of data transmitting in multi-sensor network possessing of a lot of sensors and processing sensors signals on the same time base and transmitting the results remotely, a three classes’ bus topology structure based on intelligent node was designed. The system comprised scene devices, transmission cable, and console. The scene devices included several gateways, more nodes and sensor groups. The console communicated remotely with gateways via Ethernet, and gateway communicated with nodes via CAN bus, each node managed a group of sensors via RS-485 bus. The designs of intelligent node were presented in detail including hardware design, transport protocols, as well as program flow. Above conceptual design was applied successfully in a certain multi-sensor network system principle prototype consisting of a console, a gateway and three nodes possessing of ten sensors. The results prove the conceptual design feasible.

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YAMAZAKI, HIROO. "Intelligent sensor course.(1).Present situation and future of intelligent sensors." Journal of the Fuel Society of Japan 70, no. 2 (1991): 188–94. http://dx.doi.org/10.3775/jie.70.188.

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Raj, Deepak S., and Ramesh H. S. Babu. "IFAA: An Intelligent Framework Aware Algorithm to Determine the Boundary of Area under Attack in Military Surveillance and Reconnaissance WSN." Revue d'Intelligence Artificielle 36, no. 4 (August 31, 2022): 635–40. http://dx.doi.org/10.18280/ria.360417.

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Wireless sensor networks (WSNs) have proven effective in military applications of surveillance and reconnaissance. Sensors capable of detecting pressure, temperature, movement and presence of specific chemicals are deployed in such applications. Traditionally, sensor data is collected and transferred to a centralized high-capacity node or control station. Analysis of data is carried out at such centralized facilities. Information or intelligence gathered from sensor data after analysis is used to generate control and management commands that are relayed back to sensor nodes. The situation is analogous to an actual wartime scenario where soldiers who are on the field are equivalent to the sensors. Soldiers observe and sense the situation and communicate their observations to the decision maker who is stationed in the control tent. On gathering field information, the decision maker analyses the data and arrives at his decision which is again communicated to the soldiers on the field. Soldiers as well as sensors are not placed illogically or randomly but intentionally and strategically. Observations made on the field ultimately affect how the soldiers or sensors continue to function. Intelligence gained on the field ultimately gets used on the field itself. Our attempt is to observe, analyze and apply intelligence on the field itself. This work proposes an intelligent algorithm that is aware of the sensor network topology, analyses sensor data within the network and uses the network framework to arrive at usable intelligence. Locally generated intelligence avoids communication to and from the command/control and adds value to military surveillance and reconnaissance applications of WSN. Intelligent sensor management allows us to use just the necessary number of sensors while saving resources on otherwise redundant expenditure. In the present work we have designed and applied a dynamic boundary computation algorithm to determine the boundary of the area under attack. We have compared the results of simulation experiments incorporating the proposed algorithm against a control experiment without the algorithm.

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Emura, Takashi, Masaaki Kumagai, and Lei Wang. "A Next-Generation Intelligent Car for Safe Drive." Journal of Robotics and Mechatronics 12, no. 5 (October 20, 2000): 545–51. http://dx.doi.org/10.20965/jrm.2000.p0545.

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This paper describes the fundamental development of an intelligent car whose safety is improved by using environment-sensors. Because the most important key parts in this intelligent car are environment-sensors, we developed mainly two kinds of sensors. One is a compact scanning laser range finder whose makdng cost is sufficiently low. The other is a new type ultrasonic sensor whose detectable range is very long. Because this ultrasonic sensor is a nonscanning type and detects the position of object from the phase differences of sound waves that arrive at four microphones, the detection time is short enough. We used a CCD camera together with those sensors for rapid recognition of objects and canied out basic experiments of an intelligent car.

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Guo, Yixuan, and Gaoyang Liang. "Perceptual Feedback Mechanism Sensor Technology in e-Commerce IoT Application Research." Journal of Sensors 2021 (September 28, 2021): 1–12. http://dx.doi.org/10.1155/2021/3840103.

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With the development of sensor technology and the Internet of Things (IoT) technology, the trend of miniaturization of sensors has prompted the inclusion of more sensors in IoT, and the perceptual feedback mechanism among these sensors has become particularly important, thus promoting the development of multiple sensor data fusion technologies. This paper deeply analyzes and summarizes the characteristics of sensory data and the new problems faced by the processing of sensory data under the new trend of IoT, deeply studies the acquisition, storage, and query of sensory data from the sensors of IoT in e-commerce, and proposes a ubiquitous storage method for massive sensory data by combining the sensory feedback mechanism of sensors, which makes full use of the storage resources of IoT storage network elements and maximally meets the massive. In this paper, we propose a ubiquitous storage method for massive sensing data, which makes full use of the storage resources of IoT storage network elements to maximize the storage requirements of massive sensing data and achieve load-balanced data storage. In this paper, starting from the overall development of IoT in recent years, the weak link of intelligent information processing is reinforced based on the sensory feedback mechanism of sensor technology.

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Guo, Yishan, Mingqiu Li, and Mingqiu Li. "Research on Road Condition Sensing Technology based on Vision and Radar Information." Journal of Physics: Conference Series 2400, no. 1 (December 1, 2022): 012032. http://dx.doi.org/10.1088/1742-6596/2400/1/012032.

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Abstract In the process of intelligent vehicle driving, due to the complexity of the environment, a single sensor or multiple homogeneous sensors cannot completely perceive the traffic environment around the intelligent vehicle. Therefore, it is necessary to study the information fusion scheme of different sensors and make use of the advantages of each sensor to make up for the deficiency of a single sensor, so as to realize the function of cooperation and mutual compensation between multiple sensors. In this paper, millimeter-wave radar and camera are selected as sensors for an intelligent vehicle to sense the environment ahead. Firstly, in the case of time and space synchronization of the two types of signals, fusion based on the cross-ratio of detection frames is used to find the region of common interest of radar and camera for fusion. Then the fusion based on data association is adopted. The experimental results show that the method based on radar and camera data can effectively realize accurate target recognition and location, and can meet the requirements of real-time accuracy in the application of intelligent vehicles.

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Aditi, Aditi, Ram Kinkar Pandey, Gaurav Kumar Srivastava, Nishant Anand, Kunchanapalli Rama Krishna, Prateek Singhal, and Aditi Sharma. "Intelligent Integration of Wearable Sensors and Artificial Intelligence for Real-time Athletic Performance Enhancement." Journal of Intelligent Systems and Internet of Things 13, no. 2 (2024): 60–77. http://dx.doi.org/10.54216/jisiot.130205.

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The amalgamation of wearable sensor technologies and artificial intelligence (AI) presents a transformative paradigm for optimising athletic performance in real time. This paper explores the integration of cutting-edge sensors - including bioimpedance sensors, accelerometers, and gyroscopes - with advanced AI algorithms such as machine learning and decision support systems. By capturing diverse physiological, biomechanical, and environmental data, the proposed framework aims to offer personalized, actionable insights for athletes. This research synthesizes the current landscape of wearable sensor technology in sports and highlights the evolving role of AI in interpreting data for enhancing athletic performance. It delineates an innovative framework designed for real-time analysis, personalized feedback, and training optimization. The seamless interaction between sensors and AI models empowers athletes and coaches to make informed decisions, optimizing training regimens and minimizing injury risks. The paper discusses the practical implications, challenges, and ethical considerations associated with this integration, emphasizing its potential benefits in diverse sports disciplines. Results from real-world trials underscore the efficacy of the proposed framework in providing dynamic guidance to athletes, thereby augmenting their performance through tailored interventions.

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Wang, Hao, Meng Nie, and Qing An Huang. "Design of Intelligent Meteorological System Based on MEMS." Key Engineering Materials 609-610 (April 2014): 801–6. http://dx.doi.org/10.4028/www.scientific.net/kem.609-610.801.

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Intelligent weather station system based on MEMS sensors is designed. The automatic meteorological system includes a MEMS temperature sensor, MEMS humidity sensor, MEMS pressure sensor, MEMS wind speed sensor and the sensor intelligent control system, etc. The intelligent control system has functions such as precise timing, multiple sensor data automatic acquisition, storage and uploading, which realizes the intelligent control of this weather station system.

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Lin, Yung Chin, Kuo Lan Su, and Cheng Yun Chung. "Development of Intelligent Fire Detection Module Using Optic-Sensors." Applied Mechanics and Materials 300-301 (February 2013): 440–43. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.440.

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The paper proposes an adaptive fusion algorithm using competitiveness sensors for fire detection module, and uses computer simulation results to select the optimal weight values for each optic-sensor. Then we design the fire detection module using the tuned weight values of optic-sensors. The competitiveness flame sensor type is ultra-violet sensor (R2868). The controller of the module is HOLTEK microchip, and acquires the detection signals from the optic-sensors through I/O pins, and transmits the detection signals of all sensors to the computer via wire series interface. The adaptive fusion algorithm can tunes weight values according to decision output of the fusion center. The fusion algorithms of the fusion center use Bayesian estimated method to decide the fire event to be true or not. We set the improved weight values in the module for each optic-sensor. From the simulation and experimental implementation results, it demonstrates that the proposed algorithms can compute the adequate weight values.

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Samara, Ghassan, Mohammad Hassan, and Yahya Zayed. "An Intelligent Vice Cluster Head Election Protocol in WSN." International Journal of Advances in Soft Computing and its Applications 13, no. 3 (November 28, 2021): 202–22. http://dx.doi.org/10.15849/ijasca.211128.14.

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Wireless sensor networks (WSNs) has a practical ability to link a set of sensors to build a wireless network that can be accessed remotely; this technology has become increasingly popular in recent years. Wi-Fi-enabled sensor networks (WSNs) are used to gather information from the environment in which the network operates. Many obstacles prevent wireless sensor networks from being used in a wide range of fields. This includes maintaining network stability and extending network life. In a wireless network, sensors are the most essential component. Sensors are powered by a battery that has a finite amount of power. The battery is prone to power loss, and the sensor is therefore rendered inoperative as a result. In addition, the growing number of sensor nodes off-site affects the network's stability. The transmission and reception of information between the sensors and the base consumes the most energy in the sensor. An Intelligent Vice Cluster Head Selection Protocol is proposed in this study (IVC LEACH). In order to achieve the best performance with the least amount of energy consumption, the proposed hierarchical protocol relies on a fuzzy logic algorithm using four parameters to calculate the value of each node in the network and divides them into three hierarchical levels based on their value. This improves network efficiency and reliability while extending network life by 50 percent more than the original Low Energy Adaptive Clustering Hierarchy protocol. Keywords: Wireless Sensor Networks, Sensors, Communication Protocol, Fuzzy logic, Leach protocol.

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Peng, Yiyao, Ning Yang, Qian Xu, Yang Dai, and Zhiqiang Wang. "Recent Advances in Flexible Tactile Sensors for Intelligent Systems." Sensors 21, no. 16 (August 10, 2021): 5392. http://dx.doi.org/10.3390/s21165392.

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Tactile sensors are an important medium for artificial intelligence systems to perceive their external environment. With the rapid development of smart robots, wearable devices, and human-computer interaction interfaces, flexible tactile sensing has attracted extensive attention. An overview of the recent development in high-performance tactile sensors used for smart systems is introduced. The main transduction mechanisms of flexible tactile sensors including piezoresistive, capacitive, piezoelectric, and triboelectric sensors are discussed in detail. The development status of flexible tactile sensors with high resolution, high sensitive, self-powered, and visual capabilities are focused on. Then, for intelligent systems, the wide application prospects of flexible tactile sensors in the fields of wearable electronics, intelligent robots, human-computer interaction interfaces, and implantable electronics are systematically discussed. Finally, the future prospects of flexible tactile sensors for intelligent systems are proposed.

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Hu, Lian Jun, Hong Song, Yi Luo, Xiao Hui Zeng, and Bing Qiang Wang. "The Research on Fuzzy Neural Networks in the Multi-Sensor Information Fusion of Intelligent Robots." Advanced Materials Research 225-226 (April 2011): 115–19. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.115.

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A controller based on fuzzy neural network is designed in the paper. Fuzzy neural networks are introduced into the information fusion of signals from sensors of an AS-R intelligent robot. Characteristic information of unknown environments acquired by ultrasonic sensors, infrared sensors and vision sensors are fused together in order to eliminate uncertainty caused by single sensor. Therefore, precise environment information can be obtained and the fault tolerant capabilities of robots are improved. It is proved that intelligent robots adopting multi-sensor information fusing techniques have better real-time and robust characteristics according to simulation results.

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Nicholls, H. R., J. J. Rowland, and K. A. I. Sharp. "Virtual Devices and Intelligent Gripper Control in Robotics." Robotica 7, no. 3 (July 1989): 199–204. http://dx.doi.org/10.1017/s0263574700006056.

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SUMMARYThis paper is concerned with the design of intelligent subsystems that interface actuators and sensors to intelligent supervisors for robot work-cells. Benefits of our approach include removal of low level computation and stringent real-time constraints from the superviser, potential for combining and interpreting information from sensor combinations, and provision of a uniform information interface for disparate devices. Our approach to the design and organisation of these subsystems is based on the concept of virtual devices. We demonstrate the applicability of the concept by describing the design and implementation of an intelligent controller for a sensory gripper.

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Mester, Gyula. "Intelligent wheeled mobile robot navigation." Jelenkori Társadalmi és Gazdasági Folyamatok 5, no. 1-2 (January 1, 2010): 258–64. http://dx.doi.org/10.14232/jtgf.2010.1-2.258-264.

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The paper deals with the wireless sensor-based remote control of mobile robots motion in an unknown environment with obstacles using the Sun SPOT technology and gives the fuzzy velocity control of a mobile robot motion in an unknown environment with obstacles. When the vehicle is moving towards the target and the sensors detect an obstacle, an avoiding strategy and velocity control are necessary. We proposed the wireless sensor-based remote control of mobile robots motion in an unknown environment with obstacles using the Sun SPOT technology and a fuzzy reactive navigation strategy of collision-free motion and velocity control in an unknown environment with obstacles. The simulation results show the effectiveness and the validity of the obstacle avoidance behavior in an unknown environment. The proposed method have been implemented on the miniature mobile robot Khepera® that is equipped with sensors.

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Honti, Gergely Marcell, and Janos Abonyi. "A Review of Semantic Sensor Technologies in Internet of Things Architectures." Complexity 2019 (June 12, 2019): 1–21. http://dx.doi.org/10.1155/2019/6473160.

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Intelligent sensors should be seamlessly, securely, and trustworthy interconnected to enable automated high-level smart applications. Semantic metadata can provide contextual information to support the accessibility of these features, making it easier for machines and humans to process the sensory data and achieve interoperability. The unique overview of sensor ontologies according to the semantic needs of the layers of IoT solutions can serve a guideline of engineers and researchers interested in the development of intelligent sensor-based solutions. The explored trends show that ontologies will play an even more essential role in interlinked IoT systems as interoperability and the generation of controlled linkable data sources should be based on semantically enriched sensory data.

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Ruan, Guan Qiang, Jian Cun Liu, and Wen Chao Cheng. "The Research on Electronic Smart Clutch Operating System." Applied Mechanics and Materials 380-384 (August 2013): 520–23. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.520.

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Today, the sensors are used in the different fields, especially in the automobiles and Aeronautics and Astronautics industry. Speed sensor in the car is very useful . The engine speed sensor and speed sensor are used to collect the movement of the car, which Can intelligently judge drivers true intentions of the operation, in accordance with a set of systems, intelligently solve the problem that driver can not reasonably Use the clutch. Through intelligent systems, controlling the clutch can effectively reduce the fuel consumption of the car.The system enables more intelligent operation of the vehicle, improved shift quality of the car, to improve vehicle handling and stability. Operating system with high intelligent and saving fuel is easy to operate. The operating system can effectively prevent the occurrence of car flameout. The operating system can adapt to all kinds of driver to use, especially for beginners and handicapped people to bring the gospel.

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Gao, Jinxia, Longjun Liu, Peng Gao, Yihuan Zheng, Wenxuan Hou, and Junhui Wang. "Intelligent Occlusion Stabilization Splint with Stress-Sensor System for Bruxism Diagnosis and Treatment." Sensors 20, no. 1 (December 22, 2019): 89. http://dx.doi.org/10.3390/s20010089.

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Bruxism is a masticatory muscle activity characterized by high prevalence, widespread complications, and serious consequences but without specific guidelines for its diagnosis and treatment. Although occlusal force-based biofeedback therapy is proven to be safe, effective, and with few side effects in improving bruxism, its mechanism and key technologies remain unclear. The purpose of this study was to research a real-time, quantitative, intelligent, and precise force-based biofeedback detection device based on artificial intelligence (AI) algorithms for the diagnosis and treatment of bruxism. Stress sensors were integrated and embedded into a resin-based occlusion stabilization splint by using a layering technique (sandwich method). The sensor system mainly consisted of a pressure signal acquisition module, a main control module, and a server terminal. A machine learning algorithm was leveraged for occlusal force data processing and parameter configuration. This study implemented a sensor prototype system from scratch to fully evaluate each component of the intelligent splint. Experiment results showed reasonable parameter metrics for the sensors system and demonstrated the feasibility of the proposed scheme for bruxism treatment. The intelligent occlusion stabilization splint with a stress sensor system is a promising approach to bruxism diagnosis and treatment.

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Kang, Kyeong-Sin, Oh-Sung Kwon, Wan-Ho Cho, Heung-Youl Kim, Seung-Un Chae, and Ji-Sun You. "Initial Fire Detection Method for Intelligent CCTV Using Deep Learning Technology." Fire Science and Engineering 38, no. 2 (April 30, 2024): 9–16. http://dx.doi.org/10.7731/kifse.7e1c0745.

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Pre-prediction and early detection are important for the prevention of fires. Hence, the integration of intelligent CCTV image analysis technology, deep learning, and artificial intelligence is actively being studied. Intelligent CCTV can analyze visible light images of fires, but the accuracies of the detection targets are low. To solve this problem, a dual sensing experimental device was constructed by combining a visible light sensor and a thermal image sensor, and an algorithm was implemented to predict fire events. An algorithm was constructed to detect three classes (Fire, Smoke, and Person) through RGB sensors and thermal imaging sensors. Additionally, a CNN deep learning method and the YOLOv5 method were used to build a classification neural network. The dual sensing device was able to act as a trigger to predict and detect a fire based on the size of the flame and changes in temperature.

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Sheela, Dr L. Mary Immaculate. "Sensor Fusion of Intelligent Sensors using Probability Density." International Journal of Computer Applications 12, no. 8 (December 10, 2010): 10–13. http://dx.doi.org/10.5120/1700-2205.

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VALLINO, JOSEPH J., and GREGORY N. STEPHANOPOULOS. "Intelligent Sensors in Biotechnology." Annals of the New York Academy of Sciences 506, no. 1 Biochemical E (November 1987): 415–30. http://dx.doi.org/10.1111/j.1749-6632.1987.tb23838.x.

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(ed), O. Gassmann and H. Meixner. "Sensors in Intelligent Buildings." Measurement Science and Technology 12, no. 9 (August 16, 2001): 1609. http://dx.doi.org/10.1088/0957-0233/12/9/706.

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Galambos, Colleen, Marilyn Rantz, Andy Craver, Marie Bongiorno, Michael Pelts, Austin John Holik, and Jung Sim Jun. "Living With Intelligent Sensors." CIN: Computers, Informatics, Nursing 37, no. 12 (December 2019): 615–27. http://dx.doi.org/10.1097/cin.0000000000000555.

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Haskard, Malcolm R. "General purpose intelligent sensors." Microelectronics Journal 17, no. 5 (September 1986): 9–14. http://dx.doi.org/10.1016/s0026-2692(86)80131-8.

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Costin, Hariton-Nicolae, and Saeid Sanei. "Intelligent Biosignal Processing in Wearable and Implantable Sensors." Biosensors 12, no. 6 (June 9, 2022): 396. http://dx.doi.org/10.3390/bios12060396.

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Gomes, Luis, Carlos Ramos, Aria Jozi, Bruno Serra, Lucas Paiva, and Zita Vale. "IoH: A Platform for the Intelligence of Home with a Context Awareness and Ambient Intelligence Approach." Future Internet 11, no. 3 (March 2, 2019): 58. http://dx.doi.org/10.3390/fi11030058.

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This paper presents IoH (Intelligence of Home), a platform developed to test some basic intelligent behaviors in Home context. Internet of Things, ambient intelligence and context awareness approaches motivated the development of IoH. The platform involves six layers, responsible by connectivity, persistency, unification, Internet of Things integration, subsystems integration and user interface. The integrated subsystems involve intelligent systems for light control, television brightness control, desk light control, persons counting and air conditioner control. The IoH platform is then tested for a real building, and results and conclusions are obtained. Different intelligent methods and technologies are used, form the use of a diversity of sensors, actuators, and controllers and processing units to a set of artificial intelligence approaches varying from machine learning and optimization algorithms to the use of sensor fusion and computer vision. The use of IoH day-by-day demonstrated an intelligent performance for the real building occupants.

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Ghosh, Prosenjit Kumar, and Prabha Sundaravadivel. "Stretchable Sensors for Soft Robotic Grippers in Edge-Intelligent IoT Applications." Sensors 23, no. 8 (April 17, 2023): 4039. http://dx.doi.org/10.3390/s23084039.

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The rapid development of electronic material and sensing technology has enabled research to be conducted on liquid metal-based soft sensors. The application of soft sensors is widespread and has many applications in soft robotics, smart prosthetics, and human-machine interfaces, where these sensors can be integrated for precise and sensitive monitoring. Soft sensors can be easily integrated for soft robotic applications, where traditional sensors are incompatible with robotic applications as these types of sensors show large deformation and very flexible. These liquid-metal-based sensors have been widely used for biomedical, agricultural and underwater applications. In this research, we have designed and fabricated a novel soft sensor that yields microfluidic channel arrays embedded with liquid metal Galinstan alloy. First of all, the article presents different fabrication steps such as 3D modeling, printing, and liquid metal injection. Different sensing performances such as stretchability, linearity, and durability results are measured and characterized. The fabricated soft sensor demonstrated excellent stability and reliability and exhibited promising sensitivity with respect to different pressures and conditions.

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Tabatabaei, Hamed Mofidi, Takahiro Ohashi, and Tadashi Nishihara. "Innovative Method of Embedding Optical Fiber inside Titanium Alloy Utilizing Friction Stir Forming." Key Engineering Materials 934 (November 28, 2022): 67–73. http://dx.doi.org/10.4028/p-683ks0.

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The development of FBG (Fiber Bragg Grating) sensors is essential for intelligent parts of airplanes to achieve ultra-lightweight structures and intelligent aviation control. Awaiting solution is embedding technology of optical fiber sensors into the base material of the parts; however, it has been challenging to embed fibers into high melting temperature point alloys like titanium-based materials without having any defects. Present research fulfills the mentioned demands effectively by utilizing Friction Stir Forming (FSF). Precisely, optical fiber has been placed into a guide slit inside the titanium sheet. Then, FSF was applied to the surface of the titanium. As a result, titanium plasticizes and flows into the guide slit. This mechanically interlocks the optical fiber inside the titanium alloy. This solid-state stirring process can bury sensors inside the titanium to protect the sensor from harsh environments. Embedded sensors will detect the strain and temperature of the host titanium in real-time. Moreover, this research can have further considerable effects on the areas like aerospace and artificial intelligence systems that demand real-time condition monitoring systems.

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Fernández Llorca, David, Iván García Daza, Noelia Hernández Parra, and Ignacio Parra Alonso. "Sensors and Sensing for Intelligent Vehicles." Sensors 20, no. 18 (September 8, 2020): 5115. http://dx.doi.org/10.3390/s20185115.

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Over the past decades, both industry and academy have made enormous advancements in the field of intelligent vehicles, and a considerable number of prototypes are now driving our roads, railways, air and sea autonomously. However, there is still a long way to go before a widespread adoption. Among all the scientific and technical problems to be solved by intelligent vehicles, the ability to perceive, interpret, and fully understand the operational environment, as well as to infer future states and potential hazards, represent the most difficult and complex tasks, being probably the main bottlenecks that the scientific community and industry must solve in the coming years to ensure the safe and efficient operation of the vehicles (and, therefore, their future adoption). The great complexity and the almost infinite variety of possible scenarios in which an intelligent vehicle must operate, raise the problem of perception as an "endless" issue that will always be ongoing. As a humble contribution to the advancement of vehicles endowed with intelligence, we organized the Special Issue on Intelligent Vehicles. This work offers a complete analysis of all the mansucripts published, and presents the main conclusions drawn.

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Šumak, Boštjan, Saša Brdnik, and Maja Pušnik. "Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study." Sensors 22, no. 1 (December 21, 2021): 20. http://dx.doi.org/10.3390/s22010020.

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To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.

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Albishi, Ali M., Seyed H. Mirjahanmardi, Abdulbaset M. Ali, Vahid Nayyeri, Saud M. Wasly, and Omar M. Ramahi. "Intelligent Sensing Using Multiple Sensors for Material Characterization." Sensors 19, no. 21 (November 2, 2019): 4766. http://dx.doi.org/10.3390/s19214766.

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This paper presents a concept of an intelligent sensing technique based on modulating the frequency responses of microwave near-field sensors to characterize material parameters. The concept is based on the assumption that the physical parameters being extracted such as fluid concentration are constant over the range of frequency of the sensor. The modulation of the frequency response is based on the interactions between the material under test and multiple sensors. The concept is based on observing the responses of the sensors over a frequency wideband as vectors of many dimensions. The dimensions are then considered as the features for a neural network. With small datasets, the neural networks can produce highly accurate and generalized models. The concept is demonstrated by designing a microwave sensing system based on a two-port microstrip line exciting three-identical planar resonators. For experimental validation, the sensor is used to detect the concentration of a fluid material composed of two pure fluids. Very high accuracy is achieved.

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Journal articles on the topic 'Intelligent sensors'

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