A Curvature-Based Multidirectional Local Contrast Method for Star Detection of a Star Sensor

Stray light, such as sunlight, moonlight, and earth-atmosphere light, can bring about light spots in backgrounds, and it affects the star detection of star sensors. To overcome this problem, this paper proposes a star detection algorithm (CMLCM) with multidirectional local contrast based on curvature. It regards the star image as a spatial surface and analyzes the difference in the curvature between the star and the background. It uses a facet model to represent the curvature and calculate the second-order derivatives in four directions. According to the characteristic of the star and the complex background, it enhances the target and suppresses the complex background by a new calculation method of a local contrast map. Finally, it divides the local contrast map into multiple 256 × 256 sub-regions for a more effective threshold segmentation. The experimental results indicated that the CMLCM algorithm could effectively detect a large number of accurate stars under stray light interference, and the detection rate was higher than other compared algorithms with a lower false alarm rate.

Security detection of network intrusion: application of cluster analysis method

In order to resist network malicious attacks, this paper briefly introduced the network intrusion detection model and K-means clustering analysis algorithm, improved them, and made a simulation analysis on two clustering analysis algorithms on MATLAB software. The results showed that the improved K-means algorithm could achieve central convergence faster in training, and the mean square deviation of clustering center was smaller than the traditional one in convergence. In the detection of normal and abnormal data, the improved K-means algorithm had higher accuracy and lower false alarm rate and missing report rate. In summary, the improved K-means algorithm can be applied to network intrusion detection.

Test of a Modified Infrared-Only ABI Cloud Mask Algorithm for AHI Radiance Observations

Assimilation of infrared channel radiances from geostationary imagers requires an algorithm that can separate cloudy radiances from clear-sky ones. An infrared-only cloud mask (CM) algorithm has been developed using the Advanced Himawari Imager (AHI) radiance observations. It consists of a new CM test for optically thin clouds, two modified Advanced Baseline Imager (ABI) CM tests, and seven other ABI CM tests. These 10 CM tests are used to generate composite CMs for AHI data, which are validated by using the Moderate Resolution Imaging Spectroradiometer (MODIS) CMs. It is shown that the probability of correct typing (PCT) of the new CM algorithm over ocean and over land is 89.73% and 90.30%, respectively and that the corresponding leakage rates (LR) are 6.11% and 4.21%, respectively. The new infrared-only CM algorithm achieves a higher PCT and a lower false-alarm rate (FAR) over ocean than does the Clouds from the Advanced Very High Resolution Radiometer (AVHRR) Extended System (CLAVR-x), which uses not only the infrared channels but also visible and near-infrared channels. A slightly higher FAR of 7.92% and LR of 6.18% occurred over land during daytime. This result requires further investigation.

A Blind Detection Method of Video Steganography Based on Feature Fusion

Due to lack of generalanalysis method in video digital steganalysis research area, a blind detection method which is based onfeature fusion aimed at the video steganography is proposed in this paper.Compared with special steganalysis method, the method has better detection rate,lower false alarm rate, and more extensive applicability.

Detection of manatee feeding events by animal-borne underwater sound recorders

Studies of the feeding behaviour of aquatic species in their natural environment are difficult, since direct observations are rarely possible. In this study, a newly developed animal-borne underwater sound recorder (AUSOMS-mini) was applied to captive Amazonian (Trichechus inunguis) and Antillean (Trichechus manatus manatus) manatees in order to directly record their feeding sounds. Different species of aquatic plants were offered to the manatees separately. Feeding sounds were automatically extracted using a custom program developed with MATLAB. Compared to ground truth data, the program correctly detected 65–79% of the feeding events, with a 7.3% or lower false alarm rate, which suggests that this methodology is a useful recorder of manatee feeding events. All manatees foraged during both the daytime and night-time. However, manatees tended to be less active and masticated slower during the night than during the day. The manatee mastication cycle duration depended on plant species and individual. This animal-borne acoustic monitoring system could greatly increase our knowledge of manatee feeding ecology by providing the exact time, duration and number of feeding events, and potentially the plant species being fed on.

Research on Selective Forwarding Attack Detection Scheme in Wireless Sensor Networks

Selective forwarding attacks in wireless sensor networks may corrupt some mission critical applications such as military surveillance and critical facilities monitoring. They are very difficult to be detected and distinguished from normal packet drops in wireless sensor networks. We propose an improved sequential mesh test based detection scheme in this paper. The scheme extracts a small quantity of samples to run the test, instead of regulating the total times of test in advance. We show through experiments that our scheme can provide higher detection accurate rate and lower false alarm rate than the existing detection schemes. Meanwhile, less communication and computation power are required to detect the selective forwarding attacks.

Homogenization of Temperature Series via Pairwise Comparisons

An automated homogenization algorithm based on the pairwise comparison of monthly temperature series is described. The algorithm works by forming pairwise difference series between serial monthly temperature values from a network of observing stations. Each difference series is then evaluated for undocumented shifts, and the station series responsible for such breaks is identified automatically. The algorithm also makes use of station history information, when available, to improve the identification of artificial shifts in temperature data. In addition, an evaluation is carried out to distinguish trend inhomogeneities from abrupt shifts. When the magnitude of an apparent shift attributed to a particular station can be reliably estimated, an adjustment is made for the target series. The pairwise algorithm is shown to be robust and efficient at detecting undocumented step changes under a variety of simulated scenarios with step- and trend-type inhomogeneities. Moreover, the approach is shown to yield a lower false-alarm rate for undocumented changepoint detection relative to the more common use of a reference series. Results from the algorithm are used to assess evidence for trend inhomogeneities in U.S. monthly temperature data.

An artificial neural network approach for broadband seismic phase picking

This article presents a method for picking broadband seismic phases by using backpropagation neural networks (BPNNs) as detectors. By combining the results from three BPNN detectors—long term, mid-term, and short term—the method combines the features of short term's higher accuracy and long term's lower false alarm rate. We demonstrate that proper pre- and postprocessing of the data can help to improve the system's performance. The determination of the architecture and parameters for BPNNs is also discussed in this article. The devised BPNN detector is applied to 1254 broadband seismograms of the IRIS network to determine the first arrival, which is expected to be used in tomographic studies of the mantle structure. The results show that the first arrival can be identified for more than 95% of the 1254 seismograms. The automatically picked travel times have a reasonable accuracy; more than 85% have an error of less than 1 sec, and about 80% have an error of less than 0.5 sec.