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Localization has emerged as a critical problem over the past decades, with diverse techniques developed to address robot and mobile localization challenges across varied domains. However, existing localization methods still fall short of achieving the precision needed for certain high-demand applications. The proposed algorithm is designed to enhance localization accuracy by integrating mathematical modeling with the Crow Search Algorithm (CSA). The objective is to identify the most probable position within a designated search space. Anchored by a network of fixed points, the search area is initially defined. A mathematical approach is then applied to reduce this area by calculating the intersections between circles centered at each anchor point. Within this reduced area, an array of candidate points are selected, and their centroid is computed to serve as an initial estimate. The modified CSA iteratively improves upon this estimate by emulating the natural behavior of crows, updating its variables to converge on the optimal position. Experimental evaluations, conducted on both real and simulated datasets, demonstrate that the proposed algorithm leads to a better localization accuracy than existing methods. The proposed methodology achieves a significant accuracy improvement with an accuracy of 98%. These results confirm the effectiveness of our approach for applications that require high precision with minimal infrastructure and low computational complexity.

We proposed a novel jaw movement tracking method that can measure in six degrees of freedom. The magnetic field generated by a permanent magnet paired with a small, low-power-consumption Hall effect magnetic sensor is used to estimate the relative distance between two objects—in this instance, the lower and upper jaws. By installing a microelectromechanical system (MEMS) orientation sensor in the device, we developed a mouthpiece-type sensing device that can measure voluntary mandibular movements in three-dimensional orientation and position. An evaluation of individuals wearing this device demonstrated its ability to measure mandibular movement with an accuracy of approximately 3 mm. Using the movement recording feature with six degrees of freedom also enabled the evaluation of an individual’s jaw movements over time in three dimensions. In this method, all sensors are built onto the mouthpiece and the sensing is completed in the oral cavity. It does not require the fixation of a large-scale device to the head or of a jig to the teeth, unlike existing mandibular movement tracking devices. These novel features are expected to increase the accessibility of routine measurements of natural jaw movement, unrestricted by an individual’s physiological movement and posture.

Methods to prevent collisions between people to avoid traffic accidents are receiving significant attention. To measure the position in the non-line-of-sight (NLOS) area, which cannot be directly visually recognized, position-measuring methods use wireless-communication-type GPS and propagation characteristics of radio signals, such as received signal strength indication (RSSI). However, conventional position estimation methods using RSSI require multiple receivers, which decreases the position estimation accuracy, owing to the presence of surrounding buildings. This study proposes a system to solve this challenge using a receiver and position estimation method based on RSSI MAP simulation and particle filter. Moreover, this study utilizes BLE peripheral/central functions capable of advertising as the transmitter/receiver. By using the advertising radio waves, our method provides a framework for estimating the position of unspecified transmitters. The effectiveness of the proposed system is evaluated in this study through simulations and experiments in actual environments. We obtained an error average of the distance to be 1.6 m from the simulations, which shows the precision of the proposed method. In the actual environment, the proposed method showed an error average of the distance to be 3.3 m. Furthermore, we evaluated the accuracy of the proposed method when both the transmitter and receiver are in motion, which can be considered as a moving person in the outdoor NLOS area. The result shows an error of 4.5 m. Consequently, we concluded that the accuracy was comparable when the transmitter is stationary and when it is moving. Compared with conventional path loss, the model can measure distances of 3 m to 10 m, whereas the proposed method can estimate the “position” with the same accuracy in an outdoor environment. In addition, it can be expected to be used as a collision avoidance system that confirms the presence of strangers in the NLOS area.

With the increasing use of wearable devices equipped with various sensors, information on human activities, biometrics, and surrounding environments can be obtained via sensor data at any time and place. When such devices are attached to arbitrary body parts and multiple devices are used to capture body-wide movements, it is important to estimate where the devices are attached. In this study, we propose a method that estimates the load positions of wearable devices without requiring the user to perform specific actions. The proposed method estimates the time difference between a heartbeat obtained by an ECG sensor and a pulse wave obtained by a pulse sensor, and it classifies the pulse sensor position from the estimated time difference. Data were collected at 12 body parts from four male subjects and one female subject, and the proposed method was evaluated in both user-dependent and user-independent environments. The average F-value was 1.0 when the number of target body parts was from two to five.

Sensorless position sensing of switched reluctance motor (SRM) has been of great interests to researchers for reducing costs and increasing reliability of the system. The startup position estimation is a difficult task. This study presents a new method to estimate motor phase positions during startup. It is based on the general magnetic characteristics of the inductance profile in an SRM. All phase positions are estimated without using any specific magnetic information. The calculation is simple and can be implemented easily and executed efficiently in a microcontroller.

To solve the problem of inaccurate position estimation due to the influence of magnetic saturation in sensorless control of switched reluctance motor (SRM), a new position estimation method is proposed. By analysing the linearity of the inductance curve, the region with better linearity can be found. Then, in this region, the mathematical model between rotor position and inductance can be built to estimate the rotor position by the linear fitting. However, the inductance curve will shift due to the influence of magnetic saturation in the case of heavy load, which makes the estimation error larger. Therefore, this study carries out a linear fitting of this region under different loads and establishes a linear inductance model with variable coefficient. This method can effectively eliminate the influence of saturation inductance on position estimation and have better estimation accuracy. Simulation and experimental results show the effectiveness and correctness of the proposed method.

A coherent two-dimensional (2D) multiple signal classification (MUSIC) processing for the simultaneous estimation of angular and range target positions has been presented. A 2D spatial smoothing technique is also introduced to cope with the coherent behaviour of the received echoes, which may result in a rank deficiency of the signals covariance matrix. The algorithm is analysed with respect to its application to coherent multiple-input multiple-output (MIMO) arrays. The extended baseline which is synthesised can indeed be used to further improve the performance of the system. The results of the algorithm are shown for both simulated and experimental data scenarios. In the latter case, the data are collected by a frequency-modulated continuous wave radar with MIMO functionality that has been designed and realised in cooperation with TNO, the Netherlands.

The guzheng is one of the oldest plucked instruments in China, and few studies have been conducted on the guzheng, supplemented by physics. The guzheng playing techniques involving mechanical principles mainly include, “pinching” and “shaking”. The study is based on the 3D-DGR network model to identify the three kinds of 3D dynamic finger postures. Mean shift joint localisation and displacement velocity-based target point position estimation are used to track and identify the finger movements during the playing process. At the same time, the tangent comparison method is used to detect and calculate the finger joint angles, and to judge the finger function of the performer from the angle change when applying the basic playing techniques. The recognition accuracy and the recognition time of the finger movement gesture recognition model constructed in the study are 96% and 1.07ms, respectively, which is a high recognition efficiency. The results of the evaluation of finger function by professionals are consistent with the results of the evaluation using the change of knuckle angle, which proves the recognition accuracy of the computer vision technique.

Earthing system is one of the main key elements in high-voltage safety management. It presents a safe working environment for workers and people passing by during a fault or malfunction of a power system. Commissioning the earth grid is a critical phase prior to the energizing of the high-voltage plant. Fall of potential method is commonly used to measure the earth grid resistance of the high-voltage infrastructure especially high-voltage transmission pole earth grid. The positions of the current and potential probe play a significant role that leads to precise results. This study endeavours to provide information in regards to the ‘Fall of Potential’ method. It includes the minimum separation required between the current probe and the tested grid. Furthermore, the novel location of the potential probe when the minimum separation between the current and electrode under test cannot be established is analysed. The case study results show the advanced accuracy in the tested result when deploying the novel method as provided in this study. The case study proves the advance accuracy of the proposed novel method especially when the testing area does not permit for a large separation between the tested grid and the current probe.

The 6D pose estimation of an object from an image is a central problem in many domains of Computer Vision (CV) and researchers have struggled with this issue for several years. Traditional pose estimation methods (1) leveraged on geometrical approaches, exploiting manually annotated local features, or (2) relied on 2D object representations from different points of view and their comparisons with the original image. The two methods mentioned above are also known as Feature-based and Template-based, respectively. With the diffusion of Deep Learning (DL), new Learning-based strategies have been introduced to achieve the 6D pose estimation, improving traditional methods by involving Convolutional Neural Networks (CNN). This review analyzed techniques belonging to different research fields and classified them into three main categories: Template-based methods, Feature-based methods, and Learning-Based methods. In recent years, the research mainly focused on Learning-based methods, which allow the training of a neural network tailored for a specific task. For this reason, most of the analyzed methods belong to this category, and they have been in turn classified into three sub-categories: Bounding box prediction and Perspective-n-Point (PnP) algorithm-based methods, Classification-based methods, and Regression-based methods. This review aims to provide a general overview of the latest 6D pose recovery methods to underline the pros and cons and highlight the best-performing techniques for each group. The main goal is to supply the readers with helpful guidelines for the implementation of performing applications even under challenging circumstances such as auto-occlusions, symmetries, occlusions between multiple objects, and bad lighting conditions.