Spatial Detection Method Research Articles

Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN Creations

This study proposes a novel forgery detection method based on the analysis of frequency components of images using the Discrete Fourier Transform (DFT). In recent years, face manipulation technologies, particularly Generative Adversarial Networks (GANs), have advanced to such an extent that their misuse, such as creating deepfakes indistinguishable to human observers, has become a significant societal concern. We reviewed two GAN architectures, StyleGAN and StyleGAN2, generating synthetic faces that were compared with real faces from the FFHQ and CelebA-HQ datasets. The key results demonstrate classification accuracies above 99%, with F1 scores of 99.94% for Support Vector Machines and 97.21% for Random Forest classifiers. These findings underline the fact that performing frequency analysis presents a superior approach to deepfake detection compared to traditional spatial detection methods. It provides insight into subtle manipulation cues in digital images and offers a scalable way to enhance security protocols amid rising digital impersonation threats.

A Spatial Detection Method of Underwater Acoustic Target Based on Radiated Noise Features

In order to solve the problem that the traditional sonar detection method based on beam energy is not able to detect weak targets, a feature-based spatial beam target detection method is proposed in this paper. Firstly, the feature is extracted from the pre-formed beams in the whole spatial domain, and then using the relevance vector machine (RVM) for training, the probability output of target detection is obtained. Compared with traditional methods, this method can obtain excellent detection performance under the condition of low signal-to-noise ratio or signal-to-interference ratio. The probability output of this method can better represent the uncertainty of target detection, and it is also convenient for information fusion and other processing, which is different from the output of traditional binary detection. At the end of the paper, simulated data and experimental data both proved that this method is effective.

Enhanced Spatial Spoofing Detection With and Without Direction of Arrival Estimation

Spoofing, the transmission of false (GNSS) signals, threatens receivers. The first step in dealing with spoofers is to detect the attack. Once a spoofing attack is detected, spatial detection methods using an array of antennas can additionally mitigate spoofing through spatial nulling. This article presents a spatial spoofing detection approach using the estimated (ASVs) from (DOA) estimation. Further, the method is compared to blind detection and post-DOA estimation methods. Simulation results show that the approach is comparable to the current state-of-the-art if the same a priori information is used. However, superior performance is achieved if the attitude of the antenna array is known. The presented approach represents an alternative method for spatial spoofing detection that leverages the DOA estimates but remains in the ASV domain. Therefore, it benefits from noise modeling and ambiguity resolution.

Classification of Tennis Video Types Based on Machine Learning Technology

With the rapid development of online video data, how to find the required information has become an urgent problem to be solved. This article focuses on sports videos and studies video classification and content‐based retrieval techniques. Its purpose is to establish a mark and index of video content and to promote user acquisition through computer processing, analysis, and understanding of video content. Video tennis classification has high research and application value. This article focuses on video tennis based on the selection of the basic frame of each shot and proposes an algorithm for classification of shots based on average grouping. Based on this, we use a color‐coded spatial detection method to detect the type of tennis match. Then, it integrates the results of audiovisual analysis to identify and classify exciting events in tennis matches. According to statistics, although the number of people participating in tennis cannot enter the top ten, the number of spectators ranks fourth. Four tennis tournaments, masters, and crown tournaments are held every year around the world. Watching large‐scale international tennis matches has become a pillar of leisure and vacation for many people. Tennis matches last from two hours to four hours or more, and there are countless large and small tennis matches around the world every year, so the number of tennis records created is staggering. And artificial intelligence technology is rarely used in tennis in the sports world (5%), but football has reached 50%. Therefore, when dealing with such a large amount of data, we urgently need to find a fast and effective video retrieval classification method to find the required information. The experiment of tennis video classification research based on machine learning technology proves that the accuracy of tennis video classification reaches 98%, so this system has high feasibility.

APPLICATION OF PORTABLE FLYING ROBOTIC SYSTEMS FOR EARLY DETECTION OF AN IGNITION SOURCE

Recently, the development of modern equipment and early detection of ignition sources has become relevant due to many fires and the material and human damage caused by them. This study aimed to develop a method of searching for the ignition source by moving a mobile gas analyzer towards increasing the concentration of carbon monoxide (CO) emitted in the initial stages of fire. According to the simplex and Kiefer method, an algorithm based on the spatial detection method and guaranteed trajectory definition was used to move the mobile gas analyzer towards increasing concentration. The dependence of the engine speed on the supply voltage, the angular speed of the engine with the supply voltage, the tractive force at the wing flap frequency, the supply voltage as well as the energy consumed by the engine during propulsion were calculated. To determine the direction of the azimuth towards the movement of increasing the concentration of CO, an equation was obtained that made it possible to determine the concentration of CO as a function of the distance from the carbon monoxide source. A diagram of the gradient dependence on the distance to the ignition point was plotted, and the number of points on the trajectory on which the CO concentration is measured was determined. One way to further improve early fire detection methods is to use mobile gas analyzers in the ignition source movement and determine their coordinates with the increase in CO concentration. However, further development is restricted due to insufficient research on design methods for mobile gas analyzers, communication analysis between subsystems, and calculation methods based on mathematical models that adequately describe the basic modes of movement of mobile gas analyzers.

Stress factors in the photobiology of the reef coral Siderastrea stellata

The coral species Siderastrea stellata Verril 1868 is endemic and common in Brazil. This species is a colonial, massive, zooxanthellate coral and one of the most important reef builders in many reef areas. The photosynthetic symbionts are very sensitive to environmental variations, such as sedimentation and temperature variations, which can disturb the complex interaction between the animal host and endosymbiont. In this study, we simulated the conditions found in the field in a series of experiments that evaluated chlorophyll a fluorescence emissions. Chlorophyll a maximum quantum yield and Rapid Light Curves (RLCs) were generated using a red light pulse amplitude modulated fluorometer, Mini-PAM, in S. stellata colonies under different irradiance, temperature and burial levels. Moreover, the PSII fluorescence yield was tested using a chlorophyll a fluorescence spatial detection method, which was implemented for the first time using an imaging system for S. stellata coral species. S. stellata showed great adaptive capacity under different light regimes between 50 and 160 μmol photons m-2 s-1. When a moderate burial level factor was analyzed synergistically with temperatures below 18 °C, we observed resilient colonies. However, at critical burial levels and high temperatures (28 °C) after 10 days, lower maximum quantum yields of PSII chlorophyll a fluorescence were detected, exhibiting an abrupt decrease after 96 h of burial. Continuous evaluations of coral environments and a strong understanding coral ecophysiology are extremely important for the establishment of minimum health risk levels for coral reef environments and coastal management measures. We conclude that ecophysiological aspects are highly specific and should be evaluated according to the preconditions of organism acclimatization. The chlorophyll a fluorescence imaging technique was shown to be an efficient tool for evaluating the physiologic status of zooxanthellae over the surface of a coral such as S. stellata. In turn, the photobiology results indicated the life history of this species in the upwelling waters of the region, as this species had higher Fv/Fm values at lower temperatures and intermediary burial conditions than at higher temperatures.

Probing the spatial cluster of Meriones unguiculatus using the nest flea index based on GIS technology

The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague, which can be used not only to detect the spatial and temporal distributions of Meriones unguiculatus, but also to reveal its cluster rule. This study used global spatial autocorrelation and spatial hot spot detection methods to describe the relationship between different years and the autocorrelation coefficient of nest flea indexes; it also used a spatial detection method and GIS technology to detect the spatial gathered hot spot of Meriones unguiculatus in the epidemic areas. The results of this study showed that (1) there were statistically significant spatial autocorrelations in the nest flea indexes in 2006, 2012, 2013 and 2014. (2) Most of the distribution patterns of Meriones unguiculatus were statistically significant clusters of high values. (3) There were some typical hot spot regions of plague distributed along the Inner Mongolia plateau, north of China. (4) The hot spot regions of plague were gradually stabilized after increasing and decreasing repeatedly. Generally speaking, the number of hot spot regions showed an accelerated increase from 2005 to 2007, decreased slowly from 2007 to 2008, rapidly increased again after decreasing slowly from 2008 to 2010, showed an accelerated decrease from 2010 to 2011, and ultimately were stabilized after rapidly increasing again from 2011 to 2014. (5) The migration period of the hot spot regions was 2–3 years. The epidemic area of plague moved from southwest to east during 2005, 2007, 2008 and 2010, from east to southwest during 2007 and 2008, from east to west during 2010 and 2011, and from Midwest to east during 2011 and 2014. (6) Effective factors, such as temperature, rainfall, DEM, host density, and NDVI, can affect the spatial cluster of Meriones unguiculatus. The results of this study have important implications for exploring the temporal and spatial distribution law and distribution of the hot spot regions of plague, which can reduce the risk of plague, help support the decision making process for the control and prevention of plague, and form a valuable application for plague research.

Background first- and second-order modeling for point target detection

This paper deals with point target detection in nonstationary backgrounds such as cloud scenes in aerial or satellite imaging. We propose an original spatial detection method based on first- and second-order modeling (i.e., mean and covariance) of local background statistics. We first show that state-of-the-art nonlocal denoising methods can be adapted with minimal effort to yield edge-preserving background mean estimates. These mean estimates lead to very efficient background suppression (BS) detection. However, we propose that BS be followed by a matched filter based on an estimate of the local spatial covariance matrix. The identification of these matrices derives from a robust classification of pixels in classes with homogeneous second-order statistics based on a Gaussian mixture model. The efficiency of the proposed approaches is demonstrated by evaluation on two cloudy sky background databases.

A distributed data streaming algorithm for network-wide traffic anomaly detection

Nowadays, Internet has serious security problems and network failures that are hard to resolve, for example, botnet attacks, polymorphic worm/virus spreading, DDoS, and flash crowds. To address many of these problems, we need to have a network-wide view of the traffic dynamics, and more importantly, be able to detect traffic anomaly in a timely manner. To our knowledge, Principle Component Analysis (PCA)is the best-known spatial detection method for the network-wide traffic anomaly. However, existing PCA-based solutions have scalability problems in that they require O ( m 2 n )running time and O ( mn )space to analyze traffic measurements from m aggregated traffic flows within a sliding window of the length n . We propose a novel data streaming algorithm for PCA-based network-wide traffic anomaly detection in a distributed fashion. Our algorithm can archive O ( wn log n )running time and O ( wn )space at local monitors,and O ( m 2 log n )running time and O ( m log n ) space at Network Operation Center (NOC), where w denotes the maximum number of traffic flows at a local monitor.

Detection of focal epileptiform events in the EEG by spatio-temporal dipole clustering

ObjectiveMethods for the detection of epileptiform events can be broadly divided into two main categories: temporal detection methods that exploit the EEG’s temporal characteristics, and spatial detection methods that base detection on the results of an implicit or explicit source analysis. We describe how the framework of a spatial detection method was extended to improve its performance by including temporal information. This results in a method that provides (i) automated localization of an epileptogenic focus and (ii) detection of focal epileptiform events in an EEG recording. For the detection, only one threshold value needs to be set. MethodsThe method comprises five consecutive steps: (1) dipole source analysis in a moving window, (2) automatic selection of focal brain activity, (3) dipole clustering to arrive at the identification of the epileptiform cluster, (4) derivation of a spatio-temporal template of the epileptiform activity, and (5) template matching. Routine EEG recordings from eight paediatric patients with focal epilepsy were labelled independently by two experts. The method was evaluated in terms of (i) ability to identify the epileptic focus, (ii) validity of the derived template, and (iii) detection performance. The clustering performance was evaluated using a leave-one-out cross validation. Detection performance was evaluated using Precision-Recall curves and compared to the performance of two temporal (mimetic and wavelet based) and one spatial (dipole analysis based) detection methods. ResultsThe method succeeded in identifying the epileptogenic focus in seven of the eight recordings. For these recordings, the mean distance between the epileptic focus estimated by the method and the region indicated by the labelling of the experts was 8mm. Except for two EEG recordings where the dipole clustering step failed, the derived template corresponded to the epileptiform activity marked by the experts. Over the eight EEGs, the method showed a mean sensitivity and selectivity of 92 and 77%, respectively. ConclusionsThe method allows automated localization of the epileptogenic focus and shows good agreement with the region indicated by the labelling of the experts. If the dipole clustering step is successful, the method allows a detection of the focal epileptiform events, and gave a detection performance comparable or better to that of the other methods. SignificanceThe identification and quantification of epileptiform events is of considerable importance in the diagnosis of epilepsy. Our method allows the automatic identification of the epileptic focus, which is of value in epilepsy surgery. The method can also be used as an offline exploration tool for focal EEG activity, displaying the dipole clusters and corresponding time series.

Detection of small objects in image data based on the nonlinear principal component analysis neural network

Detection of small objects in image data is fundamental to many image processing applications. Spatial domain detection of small objects is the key process of the detect before track DBT method for moving object detection. Most of the existing spatial detection methods are filter-based ones. We present a novel and efficient approach to spa- tial detection of small objects in image data, which combines the local signal-to-noise ratio SNR characteristic and appearance characteristic of small objects. In such a detection scheme, the nonlinear principal component analysis NLPCA neural network NN is used for modeling the appearance of small objects and constructing a saliency measure function. Based on this function and the feature vector extracted at each pixel position using the principal component analysis PCA technique, a small object saliency map is formed by lexicographically scanning the input image, then the saliency map is thresholded to obtain the interme- diate object location map. We also treat such a saliency map as a spa- tially filtered result of the input image. Compared to several filter-based detection methods, experiments show that the proposed algorithm out- performs these methods. © 2005 Society of Photo-Optical Instrumentation Engineers.