Success Detection Rate Research Articles

Cycle slip detection and repair for BeiDou-3 triple-frequency signals

When carrier phase observations are applied to high-precision positioning, how to handle the cycle slip is an unavoidable problem. For cycle slip correction, detection combination noise and the ionospheric delay are two crucial factors. Specifically, the drastic changes in the ionosphere and the increased noise of code observations will increase the failure probability of cycle slip detection. To reduce the influence of code observation noise and ionospheric bias, a novel cycle slip detection method for BDS-3 satellites is proposed. Considering that code measurement noise is closely related to the satellite elevation angle, an elevation-based model is built to evaluate the code measurement noise. Firstly, two modified code-phase combinations are selected optimally based on 1% missed detection rate and 99% success detection rate to minimize the effects of code measurement noise. However, the second modified code-phase combination is more affected by ionospheric delay bias, so ionospheric bias of current epoch needs to be corrected. To reduce the influence of ionospheric bias, two moving windows of time-differenced ionospheric delay are introduced to correct the ionospheric bias of the second code-phase combination. Experiments with BeiDou-3 data are implemented in three different scenarios. To verify the effectiveness of the algorithm in the environment of high code observations noise, Gaussian noise is added to the code observations in the first scenario, and the results demonstrate that the success rate of cycle slip detection and repair is still greater than 95% when the standard deviation of Gaussian noise is 0.8 m. The second scenario is carried out under low ionospheric activity, and results indicate that the proposed method significantly reduces the times of failed detection and repair. Moreover, in the third scenario, BeiDou-3 data with cycle slips of different types under high ionospheric activity are tested, and all cycle slips can be correctly detected and corrected.

A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active contour model.

In this paper, a new approach is proposed for localization and segmentation of the optic disc in human retina images. This new approach can find the boundary of the optic disc by an initial fuzzy clustering means algorithm. The proposed approach uses active contour model evolution based on a fuzzy clustering algorithm. The robustness of the proposed approach was evaluated with retinal imaging medical databases, such as DRIVE, STARE, DIARETDB1, and DRIONS. These bases contained images affected by different abnormalities, for example, diabetes, retinitis pigmentosa, and age-related macular degeneration AMD. A success detection rate with 100% accuracy was achieved for the DRIVE, DIRATEDB1, and DRIONS-DB databases, and 97.53% for the STARE database. For the optic disc segmentation, the method achieved an average accuracy and overlap in the range of 97.01-99.46% and 78.35-84.56% in these four databases. The result was compared with various methods in the literature, and it was concluded that the proposed method is more accurate than the other existing methods. Graphical abstract.

Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD.

To compare the accuracy and computational efficiency of two of the latest deep-learning algorithms for automatic identification of cephalometric landmarks. A total of 1028 cephalometric radiographic images were selected as learning data that trained You-Only-Look-Once version 3 (YOLOv3) and Single Shot Multibox Detector (SSD) methods. The number of target labeling was 80 landmarks. After the deep-learning process, the algorithms were tested using a new test data set composed of 283 images. Accuracy was determined by measuring the point-to-point error and success detection rate and was visualized by drawing scattergrams. The computational time of both algorithms was also recorded. The YOLOv3 algorithm outperformed SSD in accuracy for 38 of 80 landmarks. The other 42 of 80 landmarks did not show a statistically significant difference between YOLOv3 and SSD. Error plots of YOLOv3 showed not only a smaller error range but also a more isotropic tendency. The mean computational time spent per image was 0.05 seconds and 2.89 seconds for YOLOv3 and SSD, respectively. YOLOv3 showed approximately 5% higher accuracy compared with the top benchmarks in the literature. Between the two latest deep-learning methods applied, YOLOv3 seemed to be more promising as a fully automated cephalometric landmark identification system for use in clinical practice.

Beam Training Based on Dynamic Hierarchical Codebook for Millimeter Wave Massive MIMO

Beam training based on hierarchical codebook for millimeter wave (mmWave) massive MIMO is investigated. Unlike the existing work using the same hierarchical codebook to estimate different multi-path components (MPCs), dynamic hierarchical codebooks which are updated according to the estimated MPCs are adopted. First, a generalized hierarchical codebook design method is proposed. Then based on this method, a beam training method which dynamically updates the hierarchical codebook by removing the contribution of the estimated MPCs from the codebook is proposed. Simulation results verify the effectiveness of our method and show that the proposed method outperforms the existing ones in terms of the success detection rate of beam training.

Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants.

Extremely preterm infants often require endotracheal intubation and mechanical ventilation during the first days of life. Due to the detrimental effects of prolonged invasive mechanical ventilation (IMV), clinicians aim to extubate infants as soon as they deem them ready.Unfortunately, existing strategies for prediction of extubation readiness vary across clinicians and institutions, and lead to high reintubation rates. We present an approach using Random Forest classifiers for the analysis of cardiorespiratory variability to predict extubation readiness. We address the issue of data imbalance by employing random undersampling of examples from the majority class before training each Decision Tree in a bag. By incorporating clinical domain knowledge, we further demonstrate that our classifier could have identified 71% of infants who failed extubation, while maintaining a success detection rate of 78%.

Fully automated quantitative cephalometry using convolutional neural networks.

Quantitative cephalometry plays an essential role in clinical diagnosis, treatment, and surgery. Development of fully automated techniques for these procedures is important to enable consistently accurate computerized analyses. We study the application of deep convolutional neural networks (CNNs) for fully automated quantitative cephalometry for the first time. The proposed framework utilizes CNNs for detection of landmarks that describe the anatomy of the depicted patient and yield quantitative estimation of pathologies in the jaws and skull base regions. We use a publicly available cephalometric x-ray image dataset to train CNNs for recognition of landmark appearance patterns. CNNs are trained to output probabilistic estimations of different landmark locations, which are combined using a shape-based model. We evaluate the overall framework on the test set and compare with other proposed techniques. We use the estimated landmark locations to assess anatomically relevant measurements and classify them into different anatomical types. Overall, our results demonstrate high anatomical landmark detection accuracy ([Formula: see text] to 2% higher success detection rate for a 2-mm range compared with the top benchmarks in the literature) and high anatomical type classification accuracy ([Formula: see text] average classification accuracy for test set). We demonstrate that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry.

Left-colon water exchange preserves the benefits of whole colon water exchange at reduced cecal intubation time conferring significant advantage in diagnostic colonoscopy — a prospective, randomized controlled trial

Objectives: Whole-colon water exchange (WWE) reduces insertion pain, increases cecal intubation success and adenoma detection rate, but requires longer insertion time, compared to air insufflation (AI) colonoscopy. We hypothesized that water exchange limited to the left colon (LWE) can speed up insertion with equivalent results. Methods: This prospective, randomized controlled study (NCT01735266) allocated patients (18–80 years) to WWE, LWE or AI group (1:1:1). The primary outcome was cecal intubation time. Results: Three hundred subjects were randomized to the WWE (n = 100), LWE (n = 100) or AI group (n = 100). Ninety-four to ninety-five per cent of patients underwent diagnostic colonoscopy. Baseline characteristics were balanced. The median insertion time was shorter in LWE group (4.8 min (95%CI: 3.2–6.2)) than those in WWE (7.5 min (95%CI: 6.0–10.3)) and AI (6.4 min (95%CI: 4.2–9.8)) (both p < 0.001) groups. The cecal intubation rates in unsedated patients of the two water exchange methods (WWE 99%, LWE 99%) were significantly higher than that (89.8%) in AI group (p = 0.01). The final success rates were comparable among the three groups after sedation was given. Maximum pain scores and number of patients needing abdominal compression between WWE and LWE groups were comparable, both lower than those in AI group (p < 0.05). No significant difference was observed regarding PDR, although the PDR in right colon tended to be higher in WWE group. Conclusion: By preserving the benefits of WWE and reducing insertion time, LWE is appropriate for diagnostic colonoscopy, especially in settings with tight scheduling of patients. The higher PDR in the right colon in WWE group deserves to be further investigated.

OASys: An opportunistic and agile system to detect free on-street parking using intelligent boards embedded in surveillance cameras

This work proposes an embedded system that opportunistically detects free on-street parking by using surveillance cameras. Intelligent boards are embedded in the cameras allowing a distributed processing and communication. The task of looking for free parking spaces demands a significant amount of drivers׳ time. This problem generally takes place in urban areas where there is a large number of vehicles and few available parking spaces (on-street or off-street). Decreasing the time spent in this task could reduce the traffic jam or, in an extreme perspective, alleviate the toxic gases emission or the fossil combustible consumption. Initially, it was evaluated three classical techniques for image processing applied to detect free on-street parking. The best one presents a success detection rate close to 100%. After that, it was evaluated the performance of the embedded system by using only the best image processing technique. This performance evaluation considers three different scenarios: centralized, hybrid, and embedded. The last one is the main proposal and contribution. The results reveal that embedded proposal had an average requisition time of 2.10s vs. 0.38s of centralized one. The hybrid one presents the worst results.

IDS in Telecommunication Network using PCA

Data Security has become a very serious part of any organizational information system. Internet threats have become more intelligent so it can deceive the basic security solutions such as firewalls and antivirus scanners. To enhance the overall security of the network an additional security layer such as intrusion detection system (IDS) has to be added. The anomaly detection IDS is a type of IDS that can differentiate between normal and abnormal in the data monitored. This paper proposes two types of IDS, one of them can be used as a network intrusion detection system (NIDS) with overall success (0.9161) and high detection rate (0.9288) and the other type can also be used as a host intrusion detection system (HIDS) with overall success (0.8493) and very high detection rate (0.9628) using NSL-KDD data set.

Azimuthal propagation of seismo-magnetic signals from large earthquakes in Taiwan

This study uses a network that is comprised of 10 total intensity magnetometers to detect azimuthal propagation of seismo-magnetic emission waves during 26 earthquakes that occurred between July 2007 and December 2008 in Taiwan. The propagation azimuth and phase velocity of the seismo-magnetic waves are calculated using frequency wavenumber analysis at the ultra low frequency of 0.05 Hz every 30 min. We superimpose the derived azimuths within a moving window of 30 days as the monitored distributions, and the entire dataset as the background distribution. We also find the propagation azimuths of the seismomagnetic anomalies of each earthquake by subtracting the background from the monitored distributions. The results show that frequency wavenumber analysis can be applied to evaluate azimuthal propagation of seismo-magnetic emission waves using a scalar of geomagnetic total intensity fields. The success detection rate of seismo-magnetic anomalies increases from 62% of the 26 earthquakes to 77% using the surface magnetic anomalous reference tip (SMART) to substitute the epicenters. Meanwhile, the odds proportions between the azimuths of the seismomagnetic emission waves towards and away from SMART reveal the associated anomalous propagation.