Rotated Black Hole: A New Heuristic Optimization for Reducing Localization Error of WSN in 3D Terrain

Wireless sensor network (WSN) attracts the attention of more and more researchers, and it is applied in more and more environment. The localization information is one of the most important information in WSN. This paper proposed a novel algorithm called the rotated black hole (RBH) algorithm, which introduces a rotated optimal path and greatly improves the global search ability of the original black hole (BH) algorithm. Then, the novel algorithm is applied in reducing the localization error of WSN in 3D terrain. CEC 2013 test suit is used to verify the performance of the novel algorithm, and the simulation results show that the novel algorithm has better search performance than other famous intelligence computing algorithms. The localization simulation experiment results reveal that the novel algorithm also has an excellent performance in solving practical problems. WSN localization 3D terrain intelligence computing rotated the black hole algorithm.

Neural network assisted Kalman filter for INS/UWB integrated seamless quadrotor localization

Due to some harsh indoor environments, the signal of the ultra wide band (UWB) may be lost, which makes the data fusion filter can not work. For overcoming this problem, the neural network (NN) assisted Kalman filter (KF) for fusing the UWB and the inertial navigation system (INS) data seamlessly is present in this work. In this approach, when the UWB data is available, both the UWB and the INS are able to provide the position information of the quadrotor, and thus, the KF is used to provide the localization information by the fusion of position difference between the INS and the UWB, meanwhile, the KF can provide the estimation of the INS position error, which is able to assist the NN to build the mapping between the state vector and the measurement vector off-line. The NN can estimate the KF’s measurement when the UWB data is unavailable. For confirming the effectiveness of the proposed method, one real test has been done. The test’s results demonstrate that the proposed NN assisted KF is effective to the fusion of INS and UWB data seamlessly, which shows obvious improvement of localization accuracy. Compared with the LS-SVM assisted KF, the proposed NN assisted KF is able to reduce the localization error by about 54.34%.

Thyroid Nodules Located in the Lower Pole Have a Higher Risk of Malignancy than Located in the Isthmus: A Single-Center Experience

Purpose. The aim of our study is to investigate whether thyroid nodules (TNs) localization has value as a predictor of malignancy. Ultrasonography provides very valuable information in the evaluation of TNs, but it does not correlate perfectly with histopathologic findings. Therefore, studies that will include new diagnostic methods that can improve these unknowns can be welcomed gratefully. Methods. This study was carried out retrospectively in a tertiary care center from September 2016 to January 2020. The study included 862 adult patients who have one or more nodules. Ultrasonography of characteristics of nodules such as echogenicity, content, margins, calcifications, size, and localization was recorded. Fine-needle aspiration biopsy (FNAB) was performed on dominant and suspicious 1142 nodules. Results. The patients were composed of 692 (80.3%) females and 170 (19.7%) males. Compared to nodules located in the isthmus; the malignancy risk increased 8.39 (OR: 8.39 (2.34–30.12), p = 0.001) times in the lower pole, 4.27 (OR: 4.27 (1.16–15.72), p = 0.029), times in the middle pole, 8.09 (OR: 8.09 (2.11–30.94), p = 0.002) times in the upper pole, and 7.63 (OR: 7.63 (1.95–29.81), p = 0.003) times in the nodules covering the whole of the lobe. Although the most nodular location was in the middle pole, the risk of malignancy was less than that in the lower and upper poles. Conclusions. Unlike the other localization studies, we found a higher risk of malignancy in the lower and similarly upper thyroid poles. Besides well-defined malignancy indicators in the literature and guidelines, localization information is promising for this purpose in the future.

Dynamic Pseudo-Label Generation for Weakly Supervised Object Detection in Remote Sensing Images

In recent years, fully supervised object detection methods in remote sensing images with good performance have been developed. However, this approach requires a large number of instance-level annotated samples that are relatively expensive to acquire. Therefore, weakly supervised learning using only image-level annotations has attracted much attention. Most of the weakly supervised object detection methods are based on multi-instance learning methods, and their performance depends on the process of scoring the candidate region proposals during training. In this process, the use of only image-level labels for supervision usually cannot obtain optimal results due to the lack of location information of the object. To address the above problem, a dynamic sample pseudo-label generation framework is proposed to generate pseudo-labels for each proposal without additional annotations. First, we propose the pseudo-label generation algorithm (PLG) to generate the category labels of the proposal by using the localization information of the object. Specifically, we propose to use the pixel average of the object’s localization map in the proposal as the proposal category confidence and calculate the pseudo-label by comparing the proposal category confidence with the preset threshold. In addition, an effective adaptive threshold selection strategy is designed to eliminate the effect of different category shape differences in computing sample pseudo-labels. Comparative experiments on the NWPU VHR-10 dataset demonstrate that our method can significantly improve the detection performance compared to existing methods.

Camera-based localization microscopy optimized with calibrated structured illumination

Until very recently, super-resolution localization and tracking of fluorescent particles used camera-based wide-field imaging with uniform illumination. Then it was demonstrated that structured illuminations encode additional localization information in images. The first demonstration of this uses scanning and hence suffers from limited throughput. This limitation was mitigated by fusing camera-based localization with wide-field structured illumination. Current implementations, however, use effectively only half the localization information that they encode in images. Here we demonstrate how all of this information may be exploited by careful calibration of the structured illumination. Our approach achieves maximal resolution for given structured illumination, has a simple data analysis, and applies to any structured illumination in principle. We demonstrate this with an only slightly modified wide-field microscope. Our protocol should boost the emerging field of high-precision localization with structured illumination.

Sub-diffraction error mapping for localization microscopy images

Assessing the quality of localization microscopy images is highly challenging due to difficulty in reliably detecting errors in experimental data, with artificial sharpening being a particularly common failure mode of the technique. Here we use Haar wavelet kernel analysis (HAWK), a localization microscopy data analysis method which is known to give results without artificial sharpening, to generate a reference image. This enables the mapping and quantification of this common artefact. By suppressing intensity information, we are able to map sharpening errors in a way which is not influenced by nonlinearity in the localisation imaging process. The HAWK Method for the Assessment of Nanoscopy (HAWKMAN) is a general approach which allows the reliability of localization information to be assessed.

Dataset of spiking and LFP activity invasively recorded in the human amygdala during aversive dynamic stimuli

We present an electrophysiological dataset collected from the amygdalae of nine participants attending a visual dynamic stimulation of emotional aversive content. The participants were patients affected by epilepsy who underwent preoperative invasive monitoring in the mesial temporal lobe. Participants were presented with dynamic visual sequences of fearful faces (aversive condition), interleaved with sequences of neutral landscapes (neutral condition). The dataset contains the simultaneous recording of intracranial EEG (iEEG) and neuronal spike times and waveforms, and localization information for iEEG electrodes. Participant characteristics and trial information are provided. We technically validated this dataset and provide here the spike sorting quality metrics and the spectra of iEEG signals. This dataset allows the investigation of amygdalar response to dynamic aversive stimuli at multiple spatial scales, from the macroscopic EEG to the neuronal firing in the human brain.

Smartphone-Based Indoor Positioning Technologies

Global Navigation Satellite Systems (GNSS) have achieved great success in providing localization information in outdoor open areas. However, due to the weakness of the signal, GNSS signals cannot be received well indoors. Currently, indoor positioning plays a significant role in many areas, such as the Internet of Things (IoT) and artificial intelligence (AI), but given the complexity of indoor spaces and topology, it is still challenging to achieve an accurate, effective, full coverage and real-time positioning solution indoors. With the development of information technology, the smartphone has become more and more popular. With a large number of sensors embedded in smartphones, it is thus possible to achieve low cost, continuity, and high usability for indoor positioning. In this chapter, we focus on indoor positioning technologies with smartphones, and in particular, emphasize the technologies based on radio frequency (RF) and built-in sensors. The pros and cons of the technologies are reviewed and discussed in the context of different applications. Moreover, the challenges of indoor positioning are pointed out and the directions for the future development of this area are discussed.