Time tracking

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  • Redmine is well-known as one of the best open source project management applications. But, it's also one of the best project hosting and issue tracking solutions. In addition it incorporates Wiki, repository management, forums, time tracking, and more. This book reveals the power of Redmine and manifests its exceptional flexibility and customizability.

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Efficient Adaptive Combination of Histograms for Real-Time Tracking

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  • Due to the high cost of fossil-based energy, several methods are proposed to reduce the usage of the energy in logistics and fleet management to be even more GPS tracking system is a common approach to get vehicle location information in real-time for fleet planning

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  • The paper introduces an algorithm to design a feedback controller, which guarantees the tracking of time varying bilinear system outputs for desired values in the presence of input constraint. The proposed controller employs the ideas of receding horizon principle and constrained optimal control.

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  • WHY TRACKING AND PREDICTION ARE NEEDED IN A RADAR Let us first start by indicating why tracking and prediction are needed in a radar. Assume a fan-beam surveillance radar such as shown in Figure 1.1-1. For such a radar the fan beam rotates continually through 360  , typically with a period of 10 sec. Such a radar provides two-dimensional information about a target. The first dimension is the target range (i.e., the time it takes for a transmitted pulse to go from the transmitter to the target and back); the second dimension is the azimuth of the target,...

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  • A Real-Time Computer Vision System for Vehicle Tracking and Traffic Surveillance

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  • KALMAN FILTER 2.1 TWO-STATE KALMAN FILTER Up to now we have used a deterministic description for the target motion. Specifically, we have assumed a target having a constant-velocity motion as given by _ x nþ1 ¼ x n þ T x n _ _ x nþ1 ¼ x n ð1:1-1aÞ ð1:1-1bÞ In the real world the target will not have a constant velocity for all time. There is actually uncertainty in the target trajectory, the target accelerating or turning at any given time. Kalman allowed for this uncertainty in the target motion by adding a random component to the target dynamics [19,...

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  • LEAST-SQUARES AND MINIMUM– VARIANCE ESTIMATES FOR LINEAR TIME-INVARIANT SYSTEMS 4.1 GENERAL LEAST-SQUARES ESTIMATION RESULTS In Section 2.4 we developed (2.4-3), relating the 1 Â 1 measurement matrix Y n to the 2 Â 1 state vector X n through the 1 Â 2 observation matrix M as given by Y n ¼ MX n þ N n ð4:1-1Þ It was also pointed out in Sections 2.4 and 2.10 that this linear time-invariant equation (i.e., M is independent of time or equivalently n) applies to more general cases that we generalize further here. Specifically we assume Y n is a 1 Â ðr...

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  • GENERAL FORM FOR LINEAR TIME-INVARIANT SYSTEM 8.1 TARGET DYNAMICS DESCRIBED BY POLYNOMIAL AS A FUNCTION OF TIME 8.1.1 Introduction In Section 1.1 we defined the target dynamics model for target having a constant velocity; see (1.1-1). A constant-velocity target is one whose trajectory can be expressed by a polynomial of degree 1 in time, that is, d ¼ 1, in (5.9-1). (In turn, the tracking filter need only be of degree 1, i.e., m ¼ 1.) Alternately, it is a target for which the first derivative of its position versus time is a constant. In Section 2.4 we rewrote the target...

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  • INTRODUCTION In Section 6.3 we developed a recursive least-squares growing memory-filter for the case where the target trajectory is approximated by a polynomial. In this chapter we develop a recursive least-squares growing-memory filter that is not restricted to having the target trajectory approximated by a polynomial [5. pp. 461–482]. The only requirement is that Y nÀi , the measurement vector at time n À i, be linearly related to X nÀi in the error-free situation. The Y nÀi can be made up to multiple measurements obtained at the time n À i as in (4.

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  • NONLINEAR OBSERVATION SCHEME AND DYNAMIC MODEL (EXTENDED KALMAN FILTER) 16.1 INTRODUCTION In this section we extend the results for the linear time-invariant and timevariant cases to where the observations are nonlinearly related to the state vector and/or the target dynamics model is a nonlinear relationship [5, pp. 105– 111, 166–171, 298–300]. The approachs involve the use of linearization procedures. This linearization allows us to apply the linear least-squares and minimum-variance theory results obtained so far.

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  • BAYES ALGORITHM WITH ITERATIVE DIFFERENTIAL CORRECTION FOR NONLINEAR SYSTEMS 17.1 DETERMINATION OF UPDATED ESTIMATES We are now in a position to obtain the updated estimate for the nonlinear observation and target dynamic model cases [5, pp. 424–443]. We shall use the example of the ballistic projectile traveling through the atmosphere for definiteness in our discussion. Assume that the past measurements have " permitted us to obtain the state vector estimate Xðt À Þ at the time t À , the last time observations were made on the target. As done at the end of Section 16.

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  • HOUSEHOLDER ORTHONORMAL TRANSFORMATION In the preceding chapter we showed how the elementary Givens orthonormal transformation triangularized a matrix by successfully zeroing out one element at a time below the diagonal of each column. With the Householder orthonormal transformation all the elements below the diagonal of a given column are zeroed out simultaneously with one Householder transformation.

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  • LINEAR TIME-VARIANT SYSTEM 15.1 INTRODUCTION In this chapter we extend the results of Chapters 4 and 8 to systems having time-variant dynamic models and observation schemes [5, pp. 99–104]. For a time-varying observation system, the observation matrix M of (4.1-1) and (4.1-5) could be different at different times, that is, for different n. Thus the observation equation becomes Y n ¼ M nX n þ N n ð15:1-1Þ For a time-varying dynamics model the transition matrix È would be different at different times. In this case È of (8.

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  • Moving objects detection and tracking in video stream are basic fundamental and critical tasks in many computer vision applications. We have presented in this paper effectiveness increase of algorithms for moving objects detection and tracking. For this, we use additive minimax similarity function. Background reconstruction algorithm is developed. Moving and tracking objects detection algorithms are modified on the basis of additive minimax similarity function. Results of experiments are presented according to time expenses of the moving object detection and tracking. ...

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  • Chapter 14 Analysing Point Motion with Geographic Knowledge Discovery Techniques Patrick Laube 1, Ross S. Purves 2, Stephan Imfeld 2 and Robert Weibel 2 1 School of Geography and Environmental Science, University of Auckland, New Zealand 2 Department of Geography, University of Zurich, Switzerland 14.1 Introduction Mobility is key to contemporary life. In a globalised world, people, goods, data and ideas move in increasing volumes at increasing speeds over increasing distances, and more and more leave a digital trail behind them.

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  • Tuyển tập các báo cáo nghiên cứu khoa học ngành toán học được đăng trên tạp chí toán học quốc tế đề tài: Real-time reliability measure-driven multihypothesis tracking using 2D and 3D features

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Real-Time Model-Based Human Motion Tracking and Analysis for Human Computer Interface Systems

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  • So you want to be a big-time music producer? Whether the boatload of cash, the adoring fans, the mansions, the vacations, or the yachts is the big appeal for you, one thing is certain: You won’t be able to achieve any of these things unless you first get Home Studio installed and configured.

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  • China has a dual-track interest-rate system: bank deposit and lending rates are regulated while money and bond rates are market-determined. The central bank also imposes an indicative target, which may not be binding at all times, for total credit in the banking system. We develop and cali- brate a theoretical model to illustrate the conduct of monetary policy within the framework of dual- track interest rates and a juxtaposition of price- and quantity-based policy instruments.

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