Article of the Year 2021
Precise Blood Glucose Sensing by Nitrogen-Doped Graphene Quantum Dots for Tight Control of DiabetesRead the full article
Journal of Sensors publishes research focused on all aspects of sensors, from their theory and design, to the applications of complete sensing devices.
Chief Editor, Professor Harith Ahmad, is currently the director of the Photonics Research Center, University of Malaya, Malaysia. His current research is in the exploration of various 2D and 3D nanomaterials for optoelectronics applications.
Latest ArticlesMore articles
Multisign Health Monitoring Technology of Athletes Based on Artificial Intelligence
In order to improve the monitoring efficiency of physical fitness characteristics of athletes in training state, this paper proposes the research on athletes’ multisign health monitoring technology based on artificial intelligence sensor technology. Taking six excellent boxers as the experimental objects, four weeks of heavy load training and 10 training courses per week were arranged from six weeks before the competition to two weeks before the competition. The 4-week training is divided into two stages. From the first week to the fourth week, the training amount remains unchanged and the training intensity increases. The artificial intelligence sensor technology is used to design the athlete training state pulse test system. The athlete training state pulse sensor is responsible for collecting the athlete training state pulse data. The athlete pulse sensor in this system adopts zn17-kg200 pulse vibration sensor as the pulse sampling sensor. After 4 weeks of heavy load training, before and after adjusting for rest, venous blood was taken fasting on the next morning. White blood cells (WBC), red blood cells (RBC), hemoglobin (hemoglobin, Hb), testosterone (hemoglobin, H), cortisol (cortisol), and testosterone/cortisol ratio were measured. The experimental results showed that and of athletes were significantly lower than those before training (), 37.99% and 52.69%, respectively. and morning pulse were significantly higher than those before training (), with an increase range of 32.39% and 20.39%. There was no statistical significance in the changes of WBC, RBC, and Hb. HRV indexes lnLF and LF/HF were significantly higher than those before training. Athletes carry the designed portable athlete training state pulse test for heavy load training and display the current training state pulse data of each athlete in real time. The experimental data show that the system designed in this paper can monitor the pulse information of athletes in the training state in real time. The monitoring has good real-time result and accuracy and can realize the abnormal pulse alarm, which has a certain practical value.
AI-Enabled Energy-Efficient Fog Computing for Internet of Vehicles
Future autonomous electric vehicles (EVs) are equipped with several IoT sensors, smart devices, and wireless adapters, thus forming an Internet of Vehicles (IoVs). These intelligent EVs are envisioned to be a promising solution for improving transportation efficiency, road safety, and driving experience. Vehicular fog computing (VFC) is an evolving technology that allows vehicular application-related tasks to be offloaded to nearby computing nodes and process them quickly. A major challenge in the VFC system is to design energy-efficient task offloading algorithms. In this paper, we propose an optimal energy-efficient algorithm for task offloading in a VFC system that maximizes the expected reward function which is derived using the total energy and time delay of the system for the computation of the task. We use parallel computing and formulate the optimization problem as semi-Markov decision process (SMDP). Bellman optimal equation is used in value iteration algorithm (VIA) to get an optimal scheme by selecting the best action for the current state that maximizes the energy-based reward function. Numerical results show that the proposed scheme outperforms the greedy algorithm in terms of energy consumption.
Design and Implementation of Digital Twin-Assisted Simulation Method for Autonomous Vehicle in Car-Following Scenario
The automated system replaces the driver, which makes autonomous vehicle to improve safety and convenience, so the market of autonomous vehicle is huge. However, the real-world application of autonomous vehicles faces many challenges due to the immaturity of automated systems. As a consequence, simulation verification plays an irreplaceable role in the application of autonomous vehicle (AV). Car-following is the most common driving scenario in mixed traffic flows, so it is essential to develop an appropriate and effective simulation method for AV. Combined with the existing AV simulation methods and digital twin (DT) technology, this paper proposes a DT-assisted method for AV simulation in a car-following scenario. The method makes the physical vehicle interact with the DT vehicle, and the DT vehicle can dynamically regulate the physical entities through real-time simulation data; the simulation verification can be displayed in the DT scenario to ensure the security of the simulation. Meanwhile, a DT-assisted simulation framework of AV is proposed, the framework includes physical entity components, DT components, and data processing and evaluation components. Besides, a DT-assisted simulation platform is developed base on Unity engine. Finally, the DT-assisted simulation of AV in the car-following scenario is implemented in field experiment. The experimental results show that the proposed method can be effectively conducted AV simulation in car-following, and the average of communication latency is 52.3 ms, which is smaller than the update frequency 15 Hz (66.6 ms) between DT-assisted platform and AV. The DT-assisted simulation method of AV proposed in this paper is applied in the car-following scenario, which effectively solves the challenges of car-following scenario simulation through virtual-real interaction.
Research Progress on Humidity-Sensing Properties of Cu-Based Humidity Sensors: A Review
Novel humidity sensors based on semiconducting metal oxides with good humidity-sensing properties have attracted extensive attention, which due to their high sensitivity at room temperature, high safety, low hysteresis, and long-term stability. As a typical p-type semiconductor metal oxide, CuO is considered to be a high-performance moisture-sensitive material; however, with the development of production, the complex working environment has put forward higher requirements for its humidity sensitivity, especially sensitivity and stability. In this regard, workers around the world are working to improve the moisture-sensing properties of sensing elements. In this review, the humidity-sensing properties of CuO-based moisture-sensitive materials are comprehensively summarized, focusing on effective measures to improve the moisture-sensing properties of CuO-based moisture-sensitive materials, including surface modification and nanocomposites. The future research of semiconducting metal oxide humidity-sensitive materials is also prospected.
Innovation and Discrete Dynamic Modeling of College Music Teaching Model Based on Multiple Intelligences Theory
The development of music teaching mode in colleges and universities needs to take music as the main body and carrier to spread and inherit the music theory system. With the continuous innovation and development of science and technology, the teaching mode and teaching system have also ushered in new changes. How to let students understand the process of music teaching and better appreciate the charm of music is the main problem faced by educators. Faced with the above situation, starting from the theory of multiple intelligences, this paper studies the innovation and discrete dynamic modeling of music teaching mode in colleges and universities. Firstly, this paper discusses the application effect of this method in music teaching based on the theory of multiple intelligences. This paper investigates the actual development of multiple intelligences theory in college music teaching. Combined with the characteristics of multiple intelligences theory, modeling and analysis of students’ interest changes in the intelligent music education model represented by the space vector model are carried out. Finally, this paper studies the discrete dynamic modeling of students’ learning effect after the optimization and innovation of music teaching mode in colleges and universities under the theory of multiple intelligences. The results show that in the innovation of music teaching mode, personalized learning services should be provided based on students’ interests. The theory of multiple intelligences can help teachers to effectively analyze the diversity characteristics and changes of students in teaching activities, and it is of great help to improve students’ musical performance.
Multiobjective Optimization Strategy of WSN Coverage Based on IPSO-IRCD
The nonuniform distribution characteristic of randomly deployed mobile nodes will lead to the coverage hole and redundancy in wireless sensor networks (WSNs). To solve this problem, we propose a multiobjective optimization algorithm for WSN based on Improved Particle Swarm Optimization-Increment of the Ratio of Coverage Rate to Move Distance (IPSO-IRCD), and a network node coverage optimization model is formulated to maximize the coverage rate of the target area while reducing the moving distance of nodes. In each iteration of IPSO, the population fitness value is calculated and compared with the historical optimal value, when the arbitrary dimensional location information of each node is updated, which can avoid the standard PSO algorithm loses the optimal solution, and IPSO will determine the candidate deployment location of nodes. Based on which, IRCD node coverage scheduling optimization algorithm is proposed, so that the final deployment location can be determined iteratively by calculating IRCD of nodes. Simulation results indicate that, for the nodes initial coverage state follows random distribution and Gaussian distribution, IPSO-IRCD can, respectively, improve 4.6% and 7.4% coverage ratio compared with the suboptimal algorithm in other five similar algorithms and reduce 809.59 m and 626.63 m nodes moving distance.