جستجوی مقالات مرتبط با کلیدواژه "data fusion" در نشریات گروه "فناوری اطلاعات"
تکرار جستجوی کلیدواژه «data fusion» در نشریات گروه «فنی و مهندسی»-
In this paper, we propose a distributed tracking algorithm for a jammer-equipped target that passes through the nodes of a wireless sensor network (WSN). The jammer that the target carries is of the deceptive type, which means that it can mimic the signal of the target and confuse the sensors. Unlike other existing methods, our proposed algorithm does not require any additional hardware installation on each WSN node. It only relies on signal processing and solving the average consensus problem to detect the presence of jamming effects in the observations of the contaminated nodes and exclude them from the distributed tracking problem. For the distributed tracking problem, we use the hybrid extended Kalman filter (EKF) and particle filter to reduce the number of parameters needed for solving the average consensus problem and to decrease the communication overhead. The simulation results show that our algorithm improves the tracking performance compared to the case where the nodes with jammed observations are used.
Keywords: Distributed Particle Filter, Sensor Network, Multi-Sensor Target Tracking, Data Fusion, Consensus-Base Algorithms -
The air transport industry is seeking to manage risks in air travels. Its main objective is to detect abnormal behaviors in various flight conditions. The current methods have some limitations and are based on studying the risks and measuring the effective parameters. These parameters do not remove the dependency of a flight process on the time and human decisions. In this paper, we used an HMM-based method which is among the main methods of situation assessment in data fusion. This method includes two clustering levels based on data and model. The experiments were conducted with B_777 flight data and the variables considered in the next generation of ADS_B. According to the results of this study, our method has high speed and sensitivity in detection of abnormal changes which are effective in the flight parameters when landing. With the dynamic modelling, there is no dependency on time and conditions. The adaptation of this method to other air traffic control systems makes its extension possible to cover all flight conditions.
Keywords: Automatic Dependent Surveillance – Broadcast (ADS–B), Baum-Welch Algorithm, Data Fusion, . Expectation Maximization (EM) algorithm, Forward algorithm, Hidden Markov Model (HMM) -
In this manuscript we suggest a fast adaptive distributed method for maximum likelihood approximation (MLA) in multiple view object localization problem. For this purpose, we use "up to scale" property of projective geometry and by defining coefficients for convergence criterion, we increase the convergence speed of the consensus algorithm. We try to present a mathematical model for the problem. We use two types of error function. The proposed method uses maximum likelihood for obtaining its best parameters. Our approach utilizes "up to scale" property in projective geometry to reach the consensus quickly. The difference between node's values and meanwhile consensus values are evaluated by two error functions. To estimate consensus value in the second error function, we used local weighted average of each node. At the last of the paper, we prove our claims by experimental results.Keywords: Maximum Likelihood Approximation, Data Fusion, Consensus Algorithm, Homography
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Threat assessment in the computer networks of organizations can reduce damage caused by attacks and unexpected events. Data fusion models such as the JDL model provide efficient and adequate sensors to gather the right information at the right time from the right components. This information then is refined and normalized to provide situational awareness and assess events that may be intended as a threat. This study suggests a new method based on the JDL model where data collected from different sources is normalized into an appropriate format. After normalization, Data is converted into the information. Threat assessment unit analyzes this information based on various algorithms. We use three algorithms to detect anomaly, one to correlate alerts, and one to determine the successfulness of an attack. The model is then evaluated based on a small simulated network threat to ascertain the efficacy of the proposed method. The results show that the method is an appropriate model for situational awareness and threat assessment.Keywords: threat assessment, data fusion, situation awareness, computer networks
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پردازش چهره در اکثر کاربردهای بینایی ماشین موضوعی مهم به شمار می رود. این پردازش می تواند شامل مباحثی مثل آشکارسازی چهره، ردیابی چهره، شناخت حالات چهره و شناخت افراد شود. از میان این موارد، آشکارسازی چهره پایه ای ترین و کاربردی ترین شاخه پردازش چهره است. علت این موضوع، کاربردهای متفاوتی است که آشکارسازی چهره داراست. برای عملی کردن این کاربرد ها در ابتدا نیازمند یک الگوریتم سریع و دقیق برای آشکارسازی چهره می باشیم. روش های زیادی برای افزایش سرعت اجرای الگوریتم آشکارسازی چهره ارائه شده اند. اما معمولا این روش ها دقت نهایی سیستم را کم می کنند. در سوی مقابل روش هایی که به دنبال افزایش دقت بوده اند، با تحمیل بار محاسباتی به سیستم، میزان سرعت را پایین آورده اند. در سال های اخیر با توجه به ارزان شدن و در دسترس عموم قرار گرفتن دوربین های دریافت عمق، امکان این که بتوان در یک دقت ثابت، سرعت الگوریتم را افزایش داد، فراهم شده است. در این تحقیق ما به دنبال ایجاد یک هم جوشی مناسب بین داده های عمق و رنگ برای غلبه بر مشکلات گذشته هستیم. بدین ترتیب که از ویژگی های داده های عمق به عنوان یک کاهنده فضای جستجو استفاده کرده تا بتوان سرعت مشخص سازی ناحیه چهره را در عین حفظ دقت، افزایش داد. نتایج شبیه سازی روش پیشنهادی نشان می دهد که با استفاده از این روش، سیستم آشکارسازی چهره با حفظ دقت، حدود 2.74 برابر سریع تر نسبت به الگوریتم ویولاجونز اجرا خواهد شد. این در حالی است که آخرین روش های همه جانبه موجود به حدود 2.5 برابر افزایش سرعت رسیده اند.کلید واژگان: آشکارسازی چهره, همجوشی داده های رنگ و عمق, کینکتFace detection is an important part of many computer vision systems and has several applications in areas, such as face tracking, visual surveillance, video conferencing, face recognition, intelligent human-computer interfaces and content-based information retrieval. For use of face detection in this applications, need a fast and precise face detection algorithm. But Detection speed of traditional face detection method based on AdaBoost algorithm is slow since an exhaustive search in image. Over the past few years, the availability of color images with corresponding depth data has increased due to the popularity of low-cost RGB-Depth cameras, notably Kinect. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in face detection with intelligently constraining search over the image. In this paper, utilize additional depth data to reduce the computational cost of face detection. Leveraging the additional depth images from a Kinect camera, and use of Recurring in nature idea, we are able to accelerate the Viola-Jones face detector by 270%.Keywords: Face Detection, Data Fusion, Kinect, Depth Data
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