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检索条件"主题词=Detection Algorithms"

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Comparison between the Optimal-Ordered SIC Detector in “Low-Complexity SIC detection algorithms for Multiple-Input Multiple-Output Systems” and a Previous SIC detector

Comparison between the Optimal-Ordered SIC Detector in “Low...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Genquan Chen Hufei Zhu Fuqin Deng Yanyang Liang Faculty of Intelligent Manufacturing Wuyi University Jiangmen China

ISBN: (纸本)9781728119496

In the conclusion of the paper “Low-Complexity SIC detection algorithms for Multiple-Input Multiple-Output Systems” published in 2015, it is claimed that the proposed optimal-ordered successive interference cancellation (OSIC) detector mainly uses “fast Givens rotations” and a new method for updating the squared norms of the rows in the inverse Cholesky factor of the error covariance matrix, to achieve the lowest average complexity among the existing OSIC detectors, including a previous one published in 2011 that also utilizes the inverse Cholesky factor and Givens rotations. This paper shows that “fast Givens rotations” proposed by the above-mentioned paper in 2015 and the Givens rotations utilized in the previous detector are similar and then they should both require the same complexity. We also show that the detector proposed in 2015 and the previous detector both take the dominant complexity to compute the initial inverse Cholesky factor and triangularize the permuted inverse Cholesky factor by Givens rotations, and then the dominant complexity is not affected by the method to update the squared norms of the rows in the inverse Cholesky factor. Finally, it is concluded that both the detectors require the same dominant complexity, including the same average complexity when both of them permute the channel matrix in the same way. On the other hand, we revise the incorrect complexities quoted or claimed in the above-mentioned paper published in 2015, to show that the worst-case complexity of the OSIC detector proposed in 2015 is equal to that of the previous OSIC detector.

关键词： Interference cancellation Detectors Complexity theory detection algorithms Covariance matrices MIMO communication

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A Comparative Study on Deepfake detection algorithms

A Comparative Study on Deepfake Detection Algorithms

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Anukriti Kaushal Sanchit Singh Shashank Negi Shikhar Chhaukar Department of Computer Engineering Delhi Technological University Delhi India

In recent years, the number of images and videos shared online increased and people have easy ways to access such content. “DeepFake” refers to any multimedia content created using deep learning technology in order to make it appear realistic. The creation of deepfake videos and images using deep learning techniques leads to very realistic “DeepFake” videos and images by changing the digital content of images and videos. Deepfake is widely recognized as one of artificial intelligence’s most dangerous uses. Deepfake makes it possible to place a person in a totally imaginary situation since it is used to imitate an activity that the person did not perform. Deepfakes have been becoming increasingly dangerous to democracy, society’s security and people’s privacy. The distribution of such deepfake content on various platforms urged the international community to revaluate the threat to social security posed by such content. It encouraged the researchers around the world to develop effective deepfake detection methods. In this paper we have discussed such approaches of deepfake detection in videos and images that are available in recent studies and have provided comparative review of research on deepfake detection algorithms. It also compares the different detection techniques and examines their limitations and advantages.

关键词： Deep learning Deepfakes Privacy Streaming media Security detection algorithms

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On the False Alarm Rate of Adaptive detection algorithms for Channel Matrix-Based Cognitive Radar/Sonar

On the False Alarm Rate of Adaptive Detection Algorithms for...

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Asilomar Conference on Signals, Systems & Computers

作者： Touseef Ali Douglas Cochran Christ D. Richmond School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ Department of Electrical and Computer Engineering Duke University Durham NC

In this work, we derive an analytical expression for the probability of false alarm for the generalized likelihood ratio test (GLRT) in the channel matrix-based sonar/radar framework when the waveform-independent colored noise (WICN) covariance is assumed known. The derived expression is independent of the noise covariance and proves that the GLRT exhibits the constant probability of false alarm (CFAR) property. Monte Carlo simulations are performed to check the validity of the derived expression.

关键词： Computers Monte Carlo methods Adaptive systems Radar detection Radar Covariance matrices detection algorithms

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State-of-the-Art Analysis of Modern Drowsiness detection algorithms Based on Computer Vision

State-of-the-Art Analysis of Modern Drowsiness Detection Alg...

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Open Innovations Association (FRUCT), Conference of

作者： Fudail Hasan Alexey Kashevnik ITMO University St. Petersburg Russia SPC RAS St. Petersburg Russia

The paper presents a state-of-the-art analysis of modern drowsiness detection algorithms based on computer vision technologies as well as considers the problem of yawning detection for the vehicle driver. Based on the analysis of the literature we classify drowsiness detection techniques into three groups: the driving pattern of the vehicle; psychophysiological characteristics of drivers; and computer vision techniques for driver monitoring. The computer vision methods look most promising since they are non-intrusive for the driver. The importance of the driver drowsiness monitoring system rises from the number of drowsiness-related accidents. Yawning is an important identifier of drowsiness, even though it is not the most reliable drowsiness indicator. Some of the methods that are based on computer vision are presented and discussed in the paper. We developed and evaluated a yawning detection model. We analyzed available datasets for yawning detection and conclude that the existing datasets have to be enhanced by pictures taken in real driving conditions. We propose yawning detection dataset-preparation as well as detection model development and evaluation.

关键词： Training Computer vision Technological innovation Computational modeling Reliability Velocity measurement detection algorithms

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Image Copy-Move Forgery detection algorithms Based on Spatial Feature Domain

Image Copy-Move Forgery Detection Algorithms Based on Spatia...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Ismail Taha Ahmed Baraa Tareq Hammad Norziana Jamil College of Computer Sciences and Information Technology University of Anbar Anbar Iraq College of Computing and Informatics Universiti Tenaga Nasional Malaysia

ISBN: (数字)9781665414845

ISBN: (纸本)9781665430593

Currently, digital image forgery (DIF) become more active due to the advent of powerful image processing tools. On a daily, many images are exchanged through the internet, which makes them susceptible to such effects. One of the most popular of the passive image forgery techniques is copy-move forgery. In the Copy-move forgery, the basic process is copy/paste from one area to another in the same image. In this paper, the proposed image copy-move forgery detection (IC-MFDs) involves five stages: image preprocessing, dividing the image into overlapping blocks, calculating the mean and standard deviation of each block, feature vectors are then sorted lexicographically, then feeding the feature vector to the Support Vector Machine (SVM) classifier to identify the image as authentic or forged. Experiments are performed on a standard dataset of copy move forged images MICC-F220 to evaluate the proposed technique. The findings indicate that the proposed IC-MFDs can be extremely accurate in terms of detection Accuracy (98.44). We also compare some state-of-the-art approaches with our proposed IC-MFDs. It's noted that the findings obtained are better than these approaches.

关键词： Splicing Support vector machine classification Tools Feature extraction Forgery detection algorithms Standards

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Comparative Research of Dynamic Target detection algorithms Based on Static Background

Comparative Research of Dynamic Target Detection Algorithms ...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Chao Li Song Ran Lan Lin Tongji University Shanghai China

ISBN: (纸本)9781665409889

Dynamic target detection is a process of capturing moving objects in videos. detection results are affected by various factors that limit the algorithms’ further development. For example, mutual occlusion between objects can disrupt the overall structure of the target, which can easily lead to missed and false detections. In addition, when the light intensity increases or decreases, the pixel values of the input image also change, causing the results inaccurate. Considering the above factors, we need to understand how external conditions affect the accuracy and real-time performance of the algorithms to help us select the appropriate method in different scenes. In this paper, we propose a method for selecting suitable algorithms for different static scenes. It evaluates the number of dynamic targets and the light variation in the detected static background, which can help us select a suitable detection algorithm. First, we use the inter-frame difference, background difference, and the optical flow method to detect a single human target with a simple motion state in a closing scene. Then we apply these three algorithms to detect multiple targets in an open scene. In this situation, the algorithms need to deal with more complex information, bring a significant challenge for real-time and accuracy. During the process, the running frame rate is adjusted by modifying parameters in the experiments, improving the algorithms’ real-time performance and accuracy. Finally, we compare the performance data of these algorithms in different scenarios and give an evaluation and analysis for each algorithm. In brief, we provide a basis and reference for selecting detection algorithms for different static scenes, which is of great significance.

关键词： Costs Heuristic algorithms Dynamics Object detection Real-time systems detection algorithms Electromagnetics

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Evaluating the Effectiveness of Community detection algorithms for Influence Maximization in Social Networks

Evaluating the Effectiveness of Community Detection Algorith...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Jyothimon Chandran V Madhu Viswanatham School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9781728157917

ISBN: (纸本)9781728157924

Influence maximization, a classic optimization problem in social networks, targets to find a set of influential users capable of producing maximum influence spread under a particular diffusion model. Community structure, an important topological property of social networks, also plays a significant role in various dynamical processes, including the spreading of information in networks. Realizing the impact of community structure in information spreading, some community-based influence maximization methods have been proposed recently, and these studies show that community-based influence maximization methods perform better than the traditional influence maximization. However, the impact of the method used to detect communities on influence spread in the influence maximization problem is unknown, the need to verify and find the most effective community detection method suitable for influence maximization is essential. This paper addresses this problem by computing the influence spread of k influential nodes selected by an influential node selection method from the k largest communities extracted by the various community detection algorithms on a network. Experiments have been conducted to evaluate the performance of community detection algorithms and their impact on influence maximization on both artificial and real-world networks by employing four heuristic influence maximization methods under IC diffusion model.

关键词： Uncertainty Social networking (online) Computational modeling detection algorithms Integrated circuit modeling Optimization

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Optimizing Mobile Radio Access Network Spectrum Refarming Using Community detection algorithms 42

Optimizing Mobile Radio Access Network Spectrum Refarming Us...

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42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Budic, D. Skracic, K. Bodrusic, I. Ericsson Nikola Tesla Zagreb Croatia

ISBN: (纸本)9789532330984

Radio spectrum refarming is a cost-effective way to increase capacity for UMTS/LTE without the need to acquire new spectrum. Therefore, it is an important process for today's mobile operators. As radio access networks (RAN) became more complex, the process of choosing which radio sites to refarm has become complex and time-consuming if done manually. This paper proposes the use of community detection algorithms for automating the process of RAN spectrum refarming. The problem of RAN spectrum refarming can be modeled as a graph in which one needs to isolate nodes which are densely connected. In RAN spectrum refarming it is crucial to keep the interference between sites and technologies at a minimum. Several community detection algorithms were tested for RAN spectrum refarming. Girvan-Newman algorithm showed most accurate results and was used for implementation of optimization solution. In this paper, the authors provided a benchmark of various network and cluster sizes. Result showed that RAN spectrum refarming can be optimized from one to several weeks to roughly few hours per cluster.

关键词： Clustering algorithms detection algorithms Benchmark testing Image edge detection Partitioning algorithms Interference Radio access networks

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Hybrid neonatal EEG seizure detection algorithms achieve the benchmark of visual interpretation of the human expert 41

Hybrid neonatal EEG seizure detection algorithms achieve the...

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41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Stevenson, Nathan Tapani, Karoliina Vanhatalo, Sampsa QIMR Berghofer Med Res Inst 300 Herston Rd Brisbane Qld 4006 Australia Aalto Univ Otakaari 1 Espoo 02150 Finland Helsinki Univ Hosp Dept Neurophysiol Yliopistonkatu 4 Helsinki 00100 Finland Univ Helsinki Yliopistonkatu 4 Helsinki 00100 Finland

ISBN: (纸本)9781538613115

Neonatal EEG seizure detection algorithms (NS-DAs) have an upper bound of performance related to the agreement between visual interpretation of human experts. No published algorithms have reported performance that has reached this upper bound. In this paper, we combined two recently developed NSDAs in order to improve detection performance. An outlier detection stage was also added to improve robustness in the presence of unseen data. A large database of EEG from 79 term infants labeled by three independent human experts was used to develop and test the sufficiency of the hybrid NSDA. The inter-observer agreement (IOA) between experts was high (kappa = 0.757, 95%CI: 0.665-0.836, n=79). The area under the receiver operator characteristic of the NSDA compared to the consensus annotation of the human experts was 0.952 (95%CI: 0.0927-0.971). The IOA of seizure detection between the NSDA and human experts was not significantly less than the IOA among human experts (Delta kappa = 0.022, 95%CI: -0.020 to 0.072) and was further improved by increasing the minimum seizure duration from 10s to 30s (Delta kappa = -0.002, 95%CI: -0.073 to 0.055). Automated methods of neonatal EEG seizure detection have sufficient accuracy to replace human interpretation, potentially, providing reliable interpretations for clinicians in the neonatal intensive care unit.

关键词： Electroencephalography Pediatrics Support vector machines Feature extraction Anomaly detection Training detection algorithms

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Signal design and detection algorithms for quick detection under false alarm rate constraints 25

Signal design and detection algorithms for quick detection u...

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25th National Conference on Communications (NCC)

作者： Kumar, Alamuru Pavan Vineeth, B. S. UR Rao Satellite Ctr Payload Data Management Grp Bangalore Karnataka India Indian Inst Space Sci & Technol Dept Avion Thiruvananthapuram Kerala India

ISBN: (纸本)9781538692868

In this paper, we consider the design of sequential detection algorithms for the low delay detection of a finite duration transient change signal from noisy observations of the signal under a false alarm rate constraint. Such design problems are motivated by the need to detect explicit control signals with low delay. We propose five heuristic detection algorithms that include algorithms that directly estimate the start time of the signals. In contrast to prior work, we also consider the case where the transient change signal can be apriori designed so as to optimize the detection delay as well as false alarm rate. Using simulations and numerical studies, we compare the average delay and false alarm rate performance of the above algorithms for different choices of the transient change signals.

关键词： Delays Transient analysis detection algorithms Detectors Markov processes Signal design Noise measurement

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