Centroid Algorithm Research Articles

Exploring and Evaluating the Key Technologies in TDOA-Based Indoor Positioning

As the communication field continues to evolve, positioning technology has emerged as a crucial and valuable research avenue. Particularly, indoor positioning presents an innovative and trending direction in this technology. During the research process of indoor positioning, it is essential to consider the impact of various environmental disturbances on positioning accuracy. Additionally, efforts should be geared towards minimizing these environmental influences on the positioning system's performance and reducing the complexity of the positioning algorithm. The ideal scenario for indoor positioning involves using a relatively straightforward positioning algorithm to obtain results with high positioning accuracy. That said, it is a challenging balance to strike, given the intricate factors that influence the effectiveness of indoor positioning. This paper focuses on the Time Difference of Arrival (TDOA) localization method. We explore the TDOA-based triangle centroid algorithm, Fang's algorithm, and Chan's algorithm, all prominent methods in this field. Each of these approaches presents unique strengths and challenges, offering diverse solutions to address the complexity of indoor positioning. Their comparative analysis will provide insights into the development of efficient, accurate, and straightforward indoor positioning techniques.

Impact monitoring on complex structure using VMD-MPE feature extraction and transfer learning

Impacts are common damage events in aviation scenarios that can cause damage to the structural integrity ofan aircraft and pose a threat to its safe operation. Therefore, it is crucial to monitor impact events. A region-to-point monitoring method is proposed to address the challenges posed by the large area of monitored aircraft structures and the long distance between sensors. Firstly, to fully use the information in the original impact signal and reduce the aliasing effect caused by the reinforced structure, the original signal is decomposed into several modes with different frequency bands by Variational Mode Decomposition (VMD). The Multi-scale Permutation Entropy (MPE) value is then calculated to reflect the various characteristics of each mode, which is used as a basis for classification. Secondly, Transfer Component Analysis (TCA) is selected as a transfer learning method to reduce the difference between the features of the source domain and the target domains' features. Thirdly, the TCA-transformed source domain data are used to train the Probabilistic Neural Network model (PNN), and the unfamiliar target domain data are used to verify the impact area identification. Finally, based on regional location, the system identification technology and weighted centroid algorithm can be used to obtain the history of impact force and the precise coordinates of the impact location.

An Object SLAM Framework for Association, Mapping, and High-Level Tasks

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance.

Identification and validation of a novel HOX-related classifier signature for predicting prognosis and immune microenvironment in pediatric gliomas.

Background: Pediatric gliomas (PGs) are highly aggressive and predominantly occur in young children. In pediatric gliomas, abnormal expression of Homeobox (HOX) family genes (HFGs) has been observed and is associated with the development and progression of the disease. Studies have found that overexpression or underexpression of certain HOX genes is linked to the occurrence and prognosis of gliomas. This aberrant expression may contribute to the dysregulation of important pathological processes such as cell proliferation, differentiation, and metastasis. This study aimed to propose a novel HOX-related signature to predict patients' prognosis and immune infiltrate characteristics in PGs. Methods: The data of PGs obtained from publicly available databases were utilized to reveal the relationship among abnormal expression of HOX family genes (HFGs), prognosis, tumor immune infiltration, clinical features, and genomic features in PGs. The HFGs were utilized to identify heterogeneous subtypes using consensus clustering. Then random forest-supervised classification algorithm and nearest shrunken centroid algorithm were performed to develop a prognostic signature in the training set. Finally, the signature was validated in an internal testing set and an external independent cohort. Results: Firstly, we identified HFGs significantly differentially expressed in PGs compared to normal tissues. The individuals with PGs were then divided into two heterogeneous subtypes (HOX-SI and HOX-SII) based on HFGs expression profiles. HOX-SII showed higher total mutation counts, lower immune infiltration, and worse prognosis than HOX-SI. Then, we constructed a HOX-related gene signature (including HOXA6, HOXC4, HOXC5, HOXC6, and HOXA-AS3) based on the cluster for subtype prediction utilizing random forest supervised classification and nearest shrunken centroid algorithm. The signature was revealed to be an independent prognostic factor for patients with PGs by multivariable Cox regression analysis. Conclusion: Our study provides a novel method for the prognosis classification of PGs. The findings also suggest that the HOX-related signature is a new biomarker for the diagnosis and prognosis of patients with PGs, allowing for more accurate survival prediction.

Extreme detectable vibration frequency limited by rolling shutter camera imaging of laser speckles.

The row scanning mechanism of a rolling shutter camera can be used to infer high-frequency information from a low-frame-rate video. Combining the high intensity of laser speckle and high row-sampling rate of a rolling shutter, extreme detectable vibration frequency limited by rolling shutter camera imaging is experimentally demonstrated. Using a commercially available industrial camera at a frame rate of 70 fps, a vibration signal with a frequency of 14.285 kHz is extracted that corresponds to an inter-row sampling period of 35 µs and a sampling frequency of 28.57 kHz. Connected component and centroid alignment algorithms are used to extract the inter-row vibration displacement. The parameters that limit the highest and lowest detectable frequencies are discussed.

Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection.

This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores modifications to the distance function and centroid initialization methods to enhance image segmentation. The application of interest in the paper is the segmentation of breast tumours in mammograms. Breast cancer is the second leading cause of cancer deaths in Canadian women. Early detection reduces treatment costs and offers a favourable prognosis for patients. Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detection. Classical methods are labour-intensive, and, hence, expensive in terms of healthcare resources. Recent research supplements classical methods with automated mammogram analysis. The basic FCM method relies upon the Euclidean distance, which is not optimal for measuring non-spherical structures. To address these limitations, we review the implementation of a Mahalanobis-distance-based FCM (FCM-M). The three objectives of the paper are: (1) review FCM, FCM-M, and three centroid initialization algorithms in the literature, (2) illustrate the effectiveness of these algorithms in image segmentation, and (3) develop a Python package with the optimized algorithms to upload onto GitHub. Image analysis of the algorithms shows that using one of the three centroid initialization algorithms enhances the performance of FCM. FCM-M produced higher clustering accuracy and outlined the tumour structure better than basic FCM.

Research on fast and accurate extraction method of Brillouin frequency shift based on window-weighted centroid algorithm

Curve fitting method (CFM) is the primary method for extracting Brillouin frequency shift (BFS) in Brillouin sensing system. However, the accuracy of extraction result is affected by the initial parameter settings, the spectral shape of the Brillouin power spectrum (BPS) and the fitted curve model. Additionally, the fitting process is time-consuming. In view of this problem, this paper proposes a fast and accurate extraction method of BFS based on the window-weighted centroid algorithm (WCA). This method achieves extraction of BFS by selecting data window and calculating weighted centroid for BPS. The proposed method was experimentally verified using a Brillouin Optical Time Domain Reflectometer (BOTDR) temperature sensing system for extracting the temperature information of the 50 m heating section near the end of a 9.4 km optical fiber. Compared with CFM, cross-correlation method (XCM), backward propagation neural network (BP-NN) and centroid algorithm (CA), the proposed method has a higher accuracy and faster processing speed and is suitable for dealing with distorted non-ideal spectral shape. The proposed WCA method is an alternative method for extracting BFS information, especially in situations where the real-time requirement is high and the spectral shape is complex.

K-Means Binary Search Centroid with Dynamic Cluster for Java Island Health Clustering

This study is focused on determining the health status of each district/city in Java using the K-means Binary Search Centroid and Dynamic Kmeans algorithms. The research data uses data on the health profile of Java Island in 2020. Comparative algorithms were tested using the Davies Bound Index and Calinski-Harabasz Index methods on the traditional k-means algorithm and dynamic binary search centroid k-means. Based on the test, 5 clusters were found in the distribution area, including 11 regions with very high health quality cluster 1, 24 regions with high health quality, 28 regions with moderate health quality, and 28 clusters 4 with low health quality, 45 regions, and cluster 5 with poor health quality is 11 regions, with the best validation value of DBI 1.8175 and CHI 67.7868. Overall optimization of the dynamic k-means algorithm based on binary search centroid results in a better average cluster quality and a smaller number of iterations than the traditional k-means algorithm. The test results can be used as one of the best methods in evaluating the level of health in the Java Island area and a reference for decision-making in determining policies for related agencies.

202-LB: Association between Diabetes and the Vaginal Microbiota

Type 2 diabetes is a known risk factor for urinary tract infection and vulvovaginal candidiasis, and this risk is modulated in part by vaginal microbiota. We sought to assess the associations between diabetes and the vaginal microbiota, specifically whether diabetes may reduce vaginal Lactobacillus spp., which produce lactic acid and protect the urogenital tract. This study included 811 participants (2,301 person-visits) aged 35-60 recruited to a 2-year observational cohort with mid-vaginal sampling every 6 months. Diabetes was defined as self-reported diabetes medications, history, or serum glycated albumin (GA) levels >16.5% (HbA1c not available). Vaginal microbiota was characterized by 16S rRNA gene sequencing and clustered into community state types (CSTs) by VALENCIA, a nearest centroid algorithm based on a reference dataset. Mixed-effects multinomial logistic regression with bootstrapping was used to assess the association between case status and CSTs. Among diabetes cases (n=45, 130 person-visits), 45% had L. crispatus, L. jensenii, L. gasseri-dominated (CST I/II/V), 12% had L. iners-dominated (CST III) and 43% had low-Lactobacillus (CST IV) microbiota. Diabetes cases had reduced odds of L. iners-dominated CST III (aOR: 0.54, 95% CI: 0.31-0.81) than controls (12% vs 24%) and no association with low-Lactobacillus CST IV (aOR=01.05, 95% CI: 0.76-1.44) compared to Lactobacillus-dominated CST I/II/V in a model adjusted for menopause status and race. Estimates were similar when only GA was used for a diabetes case definition on a subset (1,001 person-visits). The results suggest diabetes is associated with reduced L. iners dominance. Diabetes cases had even frequencies of low-Lactobacillus or the optimal Lactobacillus-dominated CSTs. L. iners is metabolically distinct from other lactobacilli, producing only L-lactic acid (not the more protective D-lactic acid), and is associated with increased reproductive tract infection risk. This data informs how diabetes may affect the vaginal microenvironment. Disclosure S. J. Robbins: None. R. M. Brotman: None. R. S. Miller: None. A. L. Beitelshees: None. M. Shardell: None. J. Ravel: None. S. I. Taylor: Consultant; Ionis Pharmaceuticals. Funding National Institute of Allergy and Infectious Diseases (F31-AI164859)

K-Means Binary Search Centroid With Dynamic Cluster for Java Island Health Clustering

This study is focused on determining the health status of each district/city in Java using the K-means Binary Search Centroid and Dynamic Kmeans algorithms. The research data uses data on the health profile of Java Island in 2020. Comparative algorithms were tested using the Davies Bound Index and Calinski-Harabasz Index methods on the traditional k-means algorithm and dynamic binary search centroid k-means. Based on the test, 5 clusters were found in the distribution area, including 11 regions with very high health quality cluster 1, 24 regions with high health quality, 28 regions with moderate health quality, and 28 clusters 4 with low health quality, 45 regions, and cluster 5 with deficient health quality is 11 regions, with the best validation value of DBI 1.8175 and CHI 67.7868. Overall optimization of the dynamic k-means algorithm based on binary search centroid results in a better average cluster quality and a smaller number of iterations than the traditional k-means algorithm. The test results can be used as one of the best methods in evaluating the level of health in the Java Island area and a reference for decision-making in determining policies for related agencies

Improved Identification for Point-Distributed Coded Targets with Self-Adaption and High Accuracy in Photogrammetry

A robust and effective method for the identification of point-distributed coded targets (IPCT) in a video-simultaneous triangulation and resection system (V-STARS) was reported recently. However, its limitations were the setting of critical parameters, it being non-adaptive, making misidentifications in certain conditions, having low positioning precision, and its identification effect being slightly inferior to that of the V-STARS. Aiming to address these shortcomings of IPCT, an improved IPCT, named I-IPCT, with an adaptive binarization, a more precise ellipse-center localization, and especially an invariance of the point–line distance ratio (PLDR), was proposed. In the process of edge extraction, the adaptive threshold Gaussian function was adopted to realize the acquisition of an adaptive binarization threshold. For the process of center positioning of round targets, the gray cubic weighted centroid algorithm was adopted to realize high-precision center localization. In the template point recognition procedure, the invariant of the PLDR was used to realize the determination of template points adaptively. In the decoding procedure, the invariant of the PLDR was adopted to eliminate confusion. Experiments in indoor, outdoor, and unmanned aerial vehicle (UAV) settings were carried out; meanwhile, sufficient comparisons with IPCT and V-STARS were performed. The results show that the improvements can make the identification approximately parameter-free and more accurate. Meanwhile, it presented a high three-dimensional measurement precision in close-range photogrammetry. The improved IPCT performed equally well as the commercial software V-STARS on the whole and was slightly superior to it in the UAV test, in which it provided a fantastic open solution using these kinds of coded targets and making it convenient for researchers to freely apply the coded targets in many aspects, including UAV photogrammetry for high-precision automatic image matching and three-dimensional real-scene reconstruction.

Accurate spectrogram restoration algorithm for an echelle spectrometer based on adaptive parameters.

The echelle spectrometer is a high-resolution spectrometer that can realize transient direct readings of a full spectrum. To improve the accuracy of the spectrogram restoration model in calibration, multiple-integral time fusion, and an improved adaptive-threshold centroid algorithm are used to overcome noise and improve the accuracy of calculating the light spot position. A seven-parameter pyramid-traversal method is proposed to optimize the parameters of the spectrogram restoration model. The deviation of the spectrogram model is significantly reduced after the parameters are optimized, and the deviation curve fluctuation becomes mild, which greatly improves the model's accuracy after curve fitting.The test results show that the accuracy of the spot position determination algorithm proposed in this paper is 0.1 pixels. In addition to this, the accuracy of the spectral restoration model is controlled within 0.3 pixels in a short-wave stage and 0.7 pixels in a long-wave stage. Compared with the traditional algorithm, the accuracy of spectrogram restoration is more than two times, and the spectral calibration time is less than 45 min.

Intelligent invigilator system based on target detection.

Affected by the COVID-19 epidemic, the final examinations at many universities and the recruitment interviews of enterprises were forced to be transferred to online remote video invigilation, which undoubtedly improves the space and possibility of cheating. To solve these problems, this paper proposes an intelligent invigilation system based on the EfficientDet target detection network model combined with a centroid tracking algorithm. Experiments show that cheating behavior detection model proposed in this paper has good detection, tracking and recognition effects in remote testing scenarios. Taking the EfficientDet network as the detection target, the average detection accuracy of the network is 81%. Experiments with real online test videos show that the cheating behavior detection accuracy can reach 83.1%. In addition, to compensate for the shortage of image detection, we also design an audio detection module to carry out auxiliary detection and forensics. The audio detection module is used to continuously detect the environmental sound of the examination room, save suspicious sounds and provide evidence for judging cheating behavior.

Pulse Lidar imaging algorithm based on adaptive triangle window-width centroid discrimination

The pulse light detecting and ranging (Lidar) can obtain rich environmental information of the target, and the centroid algorithm can obtain the distance and intensity information of the target from the echo pulse. However, there are problems of sensitivity to noise and fixed window width. Therefore, this paper proposes an adaptive triangle window-width centroid algorithm (ATWC) to improve the ranging accuracy and robustness of Lidar. This algorithm combines the characteristics of the steep front and slow back of the pulse echo waveform. The centroid calculation point is selected by an isosceles triangle envelope in the time domain and a non-isosceles triangle envelope in the intensity domain with a longer front and a shorter back. Further combined with intensity weighting, the distance and intensity values of ATWC are obtained. On the one hand, the simulation verifies the influence of window width selection on the accuracy of the centroid algorithm. On the other hand, real environment comparison experiments are conducted with traditional waveform centroid (TWC), peak ranging (PR), and adaptive threshold methods (ATM) under a pulse Lidar system constructed by our method, and our method achieves significant improvements. At different distances, when the peak signal-to-noise ratio (PSNR) is above 20 dB, the ranging error of ATWC is below 0.3 ns, which is much better than the compared algorithms. When the target is at a distance of 25 m, which is a weak echo signal environment with a PSNR of 15.6 dB, our algorithm can still achieve a ranging error of 0.56 ns, and the detection success rate can still reach 88.8%. In addition, our method can obtain a clearer intensity image, which further demonstrates the research potential and practical utility of the proposed method.

Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones

Nowadays, smartphones have become indispensable in people’s daily work and life. Since various sensors and communication chips have been integrated into smartphones, it has become feasible to provide indoor positioning using phones. This paper proposes such a solution based on a smartphone, combining Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR) in the particle filter framework to realize real-time and reliable indoor positioning. First, the smartphone’s built-in accelerometer, magnetometer, and gyroscope are used to provide data measurements and formulate a feasible method for PDR. Second, a range-free weighted centroid algorithm is proposed to realize BLE-based localization with low computation complexity. However, a single positioning technology has limitations, e.g., the cumulative error of PDR and the received signal strength fluctuation of BLE. Finally, to exploit the complementary strengths of each technology, a fusion framework utilizing a particle filter is proposed to combine PDR and BLE-based methods and provides more stable and accurate positioning results. Experiments are conducted on a floor in a campus building. Experimental results show that our proposed fused positioning method offers more accurate and stable performance in the long run compared with single PDR or BLE-based positioning. The achieved average positioning error is 1.34 m, which is reduced by 24.16% compared with PDR positioning and 10.60% compared with BLE-based positioning. Moreover, about 95% of the positioning errors are smaller than 1.7 m. The proposed fused positioning method has a vast application prospect in indoor navigation, indoor user tracking, and interactive experience for indoor visitors.

Sieve Search Centroiding Algorithm for Star Sensors.

The localization of the center of the star image formed on a sensor array directly affects attitude estimation accuracy. This paper proposes an intuitive self-evolving centroiding algorithm, termed the sieve search algorithm (SSA), which employs the structural properties of the point spread function. This method maps the gray-scale distribution of the star image spot into a matrix. This matrix is further segmented into contiguous sub-matrices, referred to as sieves. Sieves comprise a finite number of pixels. These sieves are evaluated and ranked based on their degree of symmetry and magnitude. Every pixel in the image spot carries the accumulated score of the sieves associated with it, and the centroid is its weighted average. The performance evaluation of this algorithm is carried out using star images of varied brightness, spread radius, noise level, and centroid location. In addition, test cases are designed around particular scenarios, like non-uniform point spread function, stuck-pixel noise, and optical double stars. The proposed algorithm is compared with various long-standing and state-of-the-art centroiding algorithms. The numerical simulation results validated the effectiveness of SSA, which is suitable for small satellites with limited computational resources. The proposed algorithm is found to have precision comparable with that of fitting algorithms. As for computational overhead, the algorithm requires only basic math and simple matrix operations, resulting in a visible decrease in execution time. These attributes make SSA a fair compromise between prevailing gray-scale and fitting algorithms concerning precision, robustness, and processing time.

Toward a smart health: big data analytics and IoT for real-time miscarriage prediction

BackgroundWe are living in an age where data is everywhere and grows up in a very speedy way. Thanks to sensors, mobile phones and social networks, we can gather a hug amount of information to understand human behavior as well as his individual life. In healthcare system, big data analytics and machine learning algorithms prove their effectiveness and efficiency in saving lives and predicting new diseases. This triggered the idea of taking advantages of those tools and algorithms to create systems that involve both doctors and patients in the treatment of disease, predict outcomes and use real-time risk factors from sensors and mobile phones.MethodsWe distinguish three types of data: data from sensors, data from mobile phones and data registered or updated by the patient in a mobile app we created. We take advantages from IoT systems such as Raspberry Pi to collect and process data coming from sensors. All data collected is sent to a NoSql Server to be then analyzed and processed in Databricks Spark. K-means centroid clustering algorithms is used to build the predictive model, create partitions and make predictions. To validate results in term of efficiency and effectiveness, we used clustering validations techniques: Random K, Silhouette and Elbow methods.ResultsThe main contribution of our work is the implementation of a new system that has the capability to be applied in several prediction disease researches using Big Data Analytics and IoT. Also, comparing to other studies in literature that use only medical or maternal risk factors from echography; our work had the advantage to use real-time risk factors (maternal and medical) gathered from sensors, react in advance and track diseases. As a case study, we create an e-monitoring real-time miscarriage prediction system to save baby’s lives and help pregnant women. In fact, doctors receive the results of clustering and track theirs patient through our mobile app to react in term of miscarriage to avoid non-suitable outcomes. While pregnant women receive only advices based on their behaviors. The system uses 15 real-time risk factors and our dataset contains more than 1,000,000 JSON files. Elbow method affirm three as the optimal number of clusters and we reach 0.99 as a value of Silhouette method, which is a good sign that clusters are well separated and matched.

Experimental study of spatial resolution of MCPs for compact high-resolution neutron radiography system

Neutron-sensitive microchannel plates (MCPs) have been widely used as detectors in neutron radiography systems due to their high neutron detection efficiency and superior spatial resolution. These MCPs are typically employed in large-scale facilities with high neutron flux and large L/D ratios, allowing them to achieve excellent spatial resolution. However, it was uncertain whether the MCPs’ high-resolution advantage could be fully utilized in compact radiography systems with limited neutron yield and lower L/D ratios. To investigate this, thermal neutron imaging experiments were conducted in this paper. The results showed that MCPs can offer a 0.25 mm spatial resolution, compared to the 0.3 mm of a 6LiF/ZnS detector. Additionally, a centroid algorithm imaging technique was utilized to further enhance the MCPs’ spatial resolution up to 0.2 mm. This research on MCPs can also be used as a reference for other neutron radiography systems with limited L/D ratios.

Dynamic attitude measurement taken by monocular line scan vision.

Attitude measurements are important parameters for condition monitoring of precision machines. We develop an angle measurement method based on monocular line scan vision to dynamically measure the attitude of shafts. The proposed measurement system, which consists of a single line scan camera, a parallel laser light source, and a triangular mirror, does not require fixation of a sensor on the shaft. The measurement system uses the line scan camera to capture the projection of the shaft. The light source is diffused by a cylindrical lens to yield parallel directional light. Then the parallel directional light is reflected by a fixed-angle mirror and projected onto the projection plane. The captured image can be considered a discrete time sequence. The attitude measurement principle based on linear monocular vision is derived. A double light-spot centroid algorithm is developed to obtain the centroid coordinates of the projection points. The proposed method is investigated by a simulation and experiment. The proposed method can also monitor the vibration of the shaft. The experimental results prove that the proposed method is effective and highly accurate.

Research on UWB Indoor Positioning System Based on TOF Combined Residual Weighting.

The performance of TDOA positioning based on UWB is limited by the hyperbolic characteristics of TDOA, especially for tags away from the hyperbolic asymptote. Aiming at this problem, a new UWB indoor positioning system is proposed. Firstly, TOF ranging is adopted to build the positioning equations; then the weighted centroid algorithm of four base stations is presented to compute the initial rough position of the tag; and the residual weighting is introduced to optimize the initial tag position; then, the corresponding nonlinear positioning equations, which will be algebraically transformed to one distribution function, are solved, and the optimal tag coordinates can be obtained by the Newton iteration method. Simulation experiments have verified the positioning reliability of the proposed algorithm under different noise environments and for different tag positions.