Dead reckoning

A brave man\u27s unflinching attitude to his own mortality swept through the HKDI this autumn, and could have far-reaching influence on the community\u27s attitude toward death

Multistep-Ahead Neural-Network Predictors for Network Traffic Reduction in Distributed Interactive Applications

Predictive contract mechanisms such as dead reckoning are widely employed to support scalable remote entity modeling in distributed interactive applications (DIAs). By employing a form of controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the distribution of instantaneous derivative information, dead reckoning trades remote extrapolation accuracy for low computational complexity and ease-of-implementation. In this article, we present a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of instantaneous velocity information with predictive velocity information in order to improve the accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning approach, each controlling host employs a bank of neural network predictors trained to estimate future changes in entity velocity up to and including some maximum prediction horizon. The effect of each estimated change in velocity on the current entity position is simulated to produce an estimate for the likely position of the entity over some short time-span. Upon detecting an error threshold violation, the controlling host transmits a predictive velocity vector that extrapolates through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such an approach succeeds in reducing the spatial error associated with remote extrapolation of entity state. Consequently, a further reduction in network traffic can be achieved. Simulation results conducted using several human users in a highly interactive DIA indicate significant potential for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our proposed neuro-reckoning framework exhibits low computational resource overhead for real-time use and can be seamlessly integrated into many existing dead reckoning mechanisms

Multistep-Ahead Neural-Network Predictors for Network Traffic Reduction in Distributed Interactive Applications

Predictive contract mechanisms such as dead reckoning are widely employed to support scalable remote entity modeling in distributed interactive applications (DIAs). By employing a form of controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the distribution of instantaneous derivative information, dead reckoning trades remote extrapolation accuracy for low computational complexity and ease-of-implementation. In this article, we present a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of instantaneous velocity information with predictive velocity information in order to improve the accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning approach, each controlling host employs a bank of neural network predictors trained to estimate future changes in entity velocity up to and including some maximum prediction horizon. The effect of each estimated change in velocity on the current entity position is simulated to produce an estimate for the likely position of the entity over some short time-span. Upon detecting an error threshold violation, the controlling host transmits a predictive velocity vector that extrapolates through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such an approach succeeds in reducing the spatial error associated with remote extrapolation of entity state. Consequently, a further reduction in network traffic can be achieved. Simulation results conducted using several human users in a highly interactive DIA indicate significant potential for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our proposed neuro-reckoning framework exhibits low computational resource overhead for real-time use and can be seamlessly integrated into many existing dead reckoning mechanisms

Using Neural Networks to Reduce Entity State Updates in Distributed Interactive Applications

Dead reckoning is the most commonly used predictive contract mechanism for the reduction of network traffic in Distributed Interactive Applications (DIAs). However, this technique often ignores available contextual information that may be influential to the state of an entity, sacrificing remote predictive accuracy in favour of low computational complexity. In this paper, we present a novel extension of dead reckoning by employing neuralnetworks to take into account expected future entity behaviour during the transmission of entity state updates (ESUs) for remote entity modeling in DIAs. This proposed method succeeds in reducing network traffic through a decrease in the frequency of ESU transmission required to maintain consistency. Validation is achieved through simulation in a highly interactive DIA, and results indicate significant potential for improved scalability when compared to the use of the IEEE DIS Standard dead reckoning technique. The new method exhibits relatively low computational overhead and seamless integration with current dead reckoning schemes

Dead Reckoning (Book Review)

Long before she became the first female president of Harvard University in July 2007, Drew Gilpin Faust showed herself to be an inventive, energetic, and restless historian. Her first book, in 1977, focused on a subject many people had doubted was a subject, the intellectual in the Old South. Five years later, she produced what is still the fullest — and most disturbing — portrayal of a white Southern planter, a man who sought complete mastery over the white women in his charge as well as over the enslaved people he claimed as property. Soon after that, in a series of brilliant lectures, Faust challenged historians to rethink another topic many had written off as an oxymoron: Confederate nationalism. The founders of the Confederacy, she argued, adopted the latest ideals and strategies of the modern nation-state. In 1996 she published the prize-winning Mothers of Invention: Women of the Slaveholding South in the American Civil War, her most controversial book. There she put elite white Southern women in starring roles as skeptics and victims of the Confederate cause. Along the way, Faust published important essays in books edited by other scholars. In her new book, Faust does something no one has done before: She puts us face to face with death in all its dimensions in the Civil War. Books about war can hardly fail to touch on death and dying, but they can fail to look at death steadily, without blinking or looking away, as Faust has done. It is hard to imagine the sheer determination required to research such a book over a decade, the grit required to read thousands of letters from dying young men, the depth of compassion necessary to join heartbroken mothers and fathers in confronting the loss of their children. In all honesty, reading the book requires some of the same fortitude. It is customary praise to say that one can\u27t put a book down; it is even greater praise to say that one simply must set this book aside periodically

Vision-based navigation for autonomous underwater vehicles

This thesis investigates the use of vision sensors in Autonomous Underwater Vehicle (AUV) navigation, which is typically performed using a combination of dead-reckoning and external acoustic positioning systems. Traditional dead-reckoning sensors such els Doppler Velocity Logs (DVLs) or inertial systems are expensive and result in drifting trajectory estimates. Acoustic positioning systems can be used to correct dead-reckoning drift, however they are time consuming to deploy and have a limited range of operation. Occlusion and multipath problems may also occur when a vehicle operates near the seafloor, particularly in environments such as reefs, ridges and canyons, which are the focus of many AUV applications. Vision-based navigation approaches have the potential to improve the availability and performance of AUVs in a wide range of applications. Visual odometry may replace expensive dead-reckoning sensors in small and low-cost vehicles. Using onboard cameras to correct dead-reckoning drift will allow AUVs to navigate accurately over long distances, without the limitations of acoustic positioning systems. This thesis contains three principal contributions. The first is an algorithm to estimate the trajectory of a vehicle by fusing observations from sonar and monocular vision sensors. The second is a stereo-vision motion estimation approach that can be used on its own to provide odometry estimation, or fused with additional sensors in a Simultaneous Localisation And Mapping (SLAM) framework. The third is an efficient SLAM algorithm that uses visual observations to correct drifting trajectory estimates. Results of this work are presented in simulation and using data collected during several deployments of underwater vehicles in coral reef environments. Trajectory estimation is demonstrated for short transects using the sonar and vision fusion and stereo-vision approaches. Navigation over several kilometres is demonstrated using the SLAM algorithm, where stereo-vision is shown to improve the estimated trajectory produced by a DVL