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In indoor positioning systems, it is common to use existing AP deployments within buildings to build a fingerprint database, providing positioning information during the online phase. However, AP layouts inside buildings often contain a large number of redundant APs, which leads to the improvement in positioning accuracy leveling off as the number of redundant APs increases, while also increasing the computational load of indoor positioning services. To address this problem, the thesis proposes a method for calculating the AP location discrimination capability and combines the location discrimination capability with coverage to eliminate redundant APs. Experiments conducted in real indoor scenarios, as well as on the Crowdsourced dataset and the SODIndoorLoc dataset, validate the results. The results show that the redundant AP removing strategy ensures that the average positioning accuracy fluctuates by no more than 5% compared to the unfiltered case, while significantly reducing the number of APs in the fingerprint database—by 64.43%, 72.78%, and 59.62%, respectively. In the position estimation phase, this paper uses affinity propagation clustering for coarse positioning and combines Bayesian methods for fine positioning. Compared with GMM, K-Means, and the pointwise algorithm, the average positioning error of the proposed method is reduced by 11% to 39%.

In the realm of sensorless control for a permanent magnet synchronous motor (PMSM), the flux observer algorithm is widely recognized. However, the estimation accuracy of rotor position is adversely impacted by the interference from DC bias and high-order harmonics. To address these issues, an advanced flux observation method, second-order generalized integrator flux observer extend (SOGIFO-X), is introduced in this paper. The study begins with a theoretical analysis to establish the relationship between flux observation error and rotor position error. The SOGIFO-X method, developed in this study, is compared with traditional methods such as the Low Pass Filter (LPF) and second-order generalized integrator flux observer (SOGIFO), employing mathematical rigor and Bode plot analysis. The emphasis is on the methodology and the general performance improvements SOGIFO-X offers over conventional methods. Simulations and experiments were conducted to assess the impact of SOGIFO-X on the steady-state and dynamic performances of sensorless control. Findings indicate that SOGIFO-X demonstrates significant enhancements in terms of reducing the reduced flux observation error, contributing to the advancement of position estimation accuracy and sensorless motor control technology.

The performance of the extended state observer (ESO) in an Active Disturbance Rejection Control (ADRC) is limited by the operational load in stepper motor control, which has high real-time requirements and may cause delays. Additionally, the complexity of parameter tuning, especially in high-order systems, further limits the ESO’s performance. This paper proposes a composite ADRC (LTDRO-ADRC) based on a load torque dimensionality reduction observer (LTDRO). Firstly, the LTDRO is designed to estimate abrupt load disturbances that are difficult to compensate for using the ESO. Secondly, the transfer function under the double-closed loop is deduced. Additionally, the LTDRO uses a magnetic encoder to gather the system state and calculate the load torque. It then outputs a compensating current feedforward to the current loop input. This method reduces the delay and complexity of the ESO, improving the response speed of the ADRC speed ring and the overall response of the system to load changes. Simulation and experimental results demonstrate that it significantly enhances dynamic control performance and steady-state errors. LTDRO-ADRC can stabilize the speed again within 49 ms and 17 ms, respectively, in the face of sudden load increase and sudden load removal. At the same time, in terms of steady-state error, compared with ADRC and CADRC, they have increased by 94% and 88%, respectively. In terms of zero-speed starting motors, the response speed is increased by 58% compared to a traditional ADRC.

With the increasing energy demand together with the deteriorating environment and decreasing fossil fuel resources, the development of highly efficient energy conversion and storage devices is one of the key challenges of both fundamental and applied research in energy technology. Melamine sponges (MS) with low density, high nitrogen content, and high porosity have been used to design and obtain three‐dimensional porous carbon electrode materials. More importantly, they are inexpensive, environment‐friendly, and easy to synthesize. There have been many reports on the modification of carbonized MS and MS‐based composites for supercapacitor and lithium battery electrode materials. In this paper, recent studies on the fabrication of electrode materials using MS as raw materials have been mainly reviewed, including carbonation, doping activation, and composite modification of MS, and expectations for the development of porous carbon materials for energy storage as a reference with excellent performance, environment‐friendliness, and long life. Melamine sponges (MS) with high nitrogen content and porosity have been used to design three‐dimensional porous carbon electrode materials. There have been many reports on the modification of carbonized MS and MS‐based composites for supercapacitor and lithium battery electrodes materials. This paper reviewed the recent studies on the fabrication of electrode materials by using MS as raw materials, including carbonation, doping activation, and composite modification.

Assessing bladder function is pivotal in urological health, with bladder volume a critical indicator. Traditional devices, hindered by high costs and cumbersome sizes, are being increasingly supplemented by portable alternatives; however, these alternatives often fall short in measurement accuracy. Addressing this gap, this study introduces a novel A-mode ultrasound-based bladder volume estimation algorithm optimized for portable devices, combining efficient, precise volume estimation with enhanced usability. Through the innovative application of a wavelet energy ratio adaptive denoising method, the algorithm significantly improves the signal-to-noise ratio, preserving critical signal details amidst device and environmental noise. Ultrasonic echoes were employed to acquire positional information on the anterior and posterior walls of the bladder at several points, with an ellipsoid fitted to these points using the least squares method for bladder volume estimation. Ultimately, a simulation experiment was conducted on an underwater porcine bladder. The experimental results indicate that the bladder volume estimation error of the algorithm is approximately 8.3%. This study offers a viable solution to enhance the accuracy and usability of portable devices for urological health monitoring, demonstrating significant potential for clinical application.

Wireless sensor networks (WSNs) are widely applied in environmental monitoring, target tracking, military, and industrial fields. However, the battery energy of sensor nodes in WSNs is limited, which limits its development. Previous studies have shown that clustering protocols and multi-hop communication are beneficial to reduce nodes energy consumption. The multi-hop protocol based on low energy adaptive clustering hierarchy (LEACH) has been proven to significantly reduce energy dissipation. However, LEACH-based multi-hop protocols generally have the problem of unbalanced energy dissipation and data conflicts. In this paper, we propose a centralized multi-hop routing based on multi-start minimum spanning forest (LEACH-CMF) to optimize LEACH. In order to realize multi-hop communication, we introduced a multi-start minimum spanning tree algorithm to select relay nodes with the minimum relay cost and generate appropriate multi-hop paths. To avoid data collision in multi-hop communication and make nodes including the cluster heads sleep as much as possible in the non-working state, we design a bottom-up continuous time slot allocation method to improve the time division multiple access (TDMA) cycle. We performed simulation in NS2. The simulation results show that the network lifetime is approximately doubled compared to LEACH and centralized low energy adaptive clustering hierarchy (LEACH-C). The simulation results show that the proposed protocol can effectively balance the energy dissipation of nodes and prolong network lifetime.

The Multi-Point Relay (MPR) is one of the core technologies for Optimizing Link State Routing (OLSR) protocols, offering significant advantages in reducing network overhead, enhancing throughput, maintaining network scalability, and adaptability. However, due to the restriction that only MPR nodes can forward control messages in the network, the current evaluation criteria for selecting MPR nodes are relatively limited, making it challenging to flexibly choose MPR nodes based on current link states in dynamic networks. Therefore, the selection of MPR nodes is crucial in dynamic networks. To address issues such as unstable links, poor transmission accuracy, and lack of real-time performance caused by mobility in dynamic networks, we propose a comprehensive evaluation algorithm of MPR based on link-state awareness. This algorithm defines five state evaluation parameters from the perspectives of node mobility and load. Subsequently, we use the entropy weight method to determine weight coefficients and employing the method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for comprehensive evaluation to select MPR nodes. Finally, the Comprehensive Evaluation based on Link-state awareness of OLSR (CEL-OLSR) protocol is proposed, and simulated experiments are conducted using NS-3. The results indicate that, compared to PM-OLSR, ML-OLSR, LD-OLSR, and OLSR, CEL-OLSR significantly improves network performance in terms of packet delivery rate, average end-to-end delay, network throughput, and control overhead.

Node localization technology is one of the most important technologies in wireless sensor networks. Due to the advantages of saving and convenience, received signal strength indication (RSSI) technology is widely taken to measure the distance between the sensor nodes, and then trilateral localization algorithm which is one of the classic algorithms can calculate the position result quickly. However, the result always comes with an irregularly wide error. Environment, temperature and electromagnetism are generally considered the interference factors, which have been widely researched. From another angle, this study focuses on the error of the algorithm itself, and discusses the stability of equations. A trilateral localization algorithm for outliers suppression is proposed. Through a large number of simulation, it is demonstrated that the proposed algorithm has a good performance than classic trilateral algorithm based-on the nearest three anchor nodes. A significant meaning of this research is that the deepest source of gross errors has been found when we use classic trilateral algorithm.

Hierarchical SnO2/Fe2O3 hollow spheres are fabricated through the solvothermal method accompanied by a facile solution procedure in the absence of any surfactant. The composition, crystalline structure, morphology, and electrochemical properties of the synthesized product are investigated systematically. The SnO2/Fe2O3 as an anode material in Li‐ion batteries demonstrates a better reversible capacity and very good rate capability and cyclic performance compared with pure‐phase SnO2 hollow spheres. The greatly enhanced Li‐ion storage capability of the SnO2/Fe2O3 hollow spheres is believed to result from the synergy of the two materials, SnO2 and Fe2O3, which provides breathable aggregates for effective buffering of the drastic volume changes during the charge/discharge process. This work reports a promising method for the design and preparation of nanocomposite electrodes for Li‐ion batteries. Nanocomposite electrodes: Uniform SnO2/Fe2O3 hollow spheres (see figure) are obtained through the solvothermal method accompanied by a simple solution route. These hollow spheres display a higher capacity, better rate capability, and more stable cyclic performance than those of pure‐phase SnO2, and may be suitable for use as the anode in lithium‐ion batteries.

With the increasing energy demand together with the deteriorating environment and decreasing fossil fuel resources, the development of highly efficient energy conversion and storage devices is one of the key challenges of both fundamental and applied research in energy technology. Melamine sponges (MS) with low density, high nitrogen content, and high porosity have been used to design and obtain three‐dimensional porous carbon electrode materials. More importantly, they are inexpensive, environment‐friendly, and easy to synthesize. There have been many reports on the modification of carbonized MS and MS‐based composites for supercapacitor and lithium battery electrode materials. In this paper, recent studies on the fabrication of electrode materials using MS as raw materials have been mainly reviewed, including carbonation, doping activation, and composite modification of MS, and expectations for the development of porous carbon materials for energy storage as a reference with excellent performance, environment‐friendliness, and long life.