A method for atomistic spin dynamics simulations: implementation and examples

We present a method for performing atomistic spin dynamic simulations. A comprehensive summary of all pertinent details for performing the simulations such as equations of motions, models for including temperature, methods of extracting data and numerical schemes for performing the simulations is given. The method can be applied in a first principles mode, where all interatomic exchange is calculated self-consistently, or it can be applied with frozen parameters estimated from experiments or calculated for a fixed spin-configuration. Areas of potential applications to different magnetic questions are also discussed. The method is finally applied to one situation where the macrospin model breaks down; magnetic switching in ultra strong fields.Comment: 14 pages, 19 figure

Current driven magnetization dynamics in helical spin density waves

A mechanism is proposed for manipulating the magnetic state of a helical spin density wave using a current. In this paper, we show that a current through a bulk system with a helical spin density wave induces a spin transfer torque, giving rise to a rotation of the order parameter.The use of spin transfer torque to manipulate the magnetization in bulk systems does not suffer from the obstacles seen for magnetization reversal using interface spin transfer torque in multilayered systems. We demonstrate the effect by a quantitative calculation of the current induced magnetization dynamics of Erbium. Finally we propose a setup for experimental verification.Comment: In the previous version of this paper was a small numerical mistake made when evaluating equation 3 and 9. The number of digits given in the calculation of the torque current tensor is reduced to better represent the accuracy of the calculation. A slightly modified paper have been published in Phys. Rev. Lett. 96, 256601 (2006) 4 pages 3 figure

Resident Identification using Kinect Depth Image Data and Fuzzy Clustering Techniques

As a part of our passive fall risk assessment research in home environments, we present a method to identify older residents using features extracted from their gait information from a single depth camera. Depth images have been collected continuously for about eight months from several apartments at a senior housing facility. Shape descriptors such as bounding box information and image moments were extracted from silhouettes of the depth images. The features were then clustered using Possibilistic C Means for resident identification. This technology will allow researchers and health professionals to gather more information on the individual residents by filtering out data belonging to non-residents. Gait related information belonging exclusively to the older residents can then be gathered. The data can potentially help detect changes in gait patterns which can be used to analyze fall risk for elderly residents by passively observing them in their home environments

Sit-to-Stand Detection using Fuzzy Clustering Techniques

The ability to rise from a chair is an important parameter to assess the balance deficits of a person. In particular, this can be an indication of risk for falling in elderly persons. Our goal is automated assessment of fall risk using video data. Towards this goal, we present a simple yet effective method of detecting transition, i.e. sit-to-stand and stand-to-sit, from image frames using fuzzy clustering methods on image moments. The technique described in this paper is shown to be robust even in the presence of noise and has been tested on several data sequences using different subjects yielding promising results

Performance of an Operating High Energy Physics Data Grid: D0SAR-Grid

The D0 experiment at Fermilab's Tevatron will record several petabytes of data over the next five years in pursuing the goals of understanding nature and searching for the origin of mass. Computing resources required to analyze these data far exceed capabilities of any one institution. Moreover, the widely scattered geographical distribution of D0 collaborators poses further serious difficulties for optimal use of human and computing resources. These difficulties will exacerbate in future high energy physics experiments, like the LHC. The computing grid has long been recognized as a solution to these problems. This technology is being made a more immediate reality to end users in D0 by developing a grid in the D0 Southern Analysis Region (D0SAR), D0SAR-Grid, using all available resources within it and a home-grown local task manager, McFarm. We will present the architecture in which the D0SAR-Grid is implemented, the use of technology and the functionality of the grid, and the experience from operating the grid in simulation, reprocessing and data analyses for a currently running HEP experiment.Comment: 3 pages, no figures, conference proceedings of DPF04 tal

Adaptive Silhouette Extraction In Dynamic Environments Using Fuzzy Logic

Extracting a human silhouette from an image is the enabling step for many high-level vision processing tasks, such as human tracking and activity analysis. In a previous paper, we addressed some of the challenges in silhouette extraction and human tracking in a real-world unconstrained environment where the background is complex and dynamic. We extracted features from image regions, accumulated the feature information over time, fused high-level knowledge with low-level features, and built a time-varying background model. A problem with our system is that by adapting the background model, objects moved by a human are difficult to handle. In order to reinsert them into the background, we run the risk of cutting off part of the human silhouette, such as in a quick arm movement. In this paper, we develop a fuzzy logic inference system to detach the silhouette of a moving object from the human body. Our experimental results demonstrate that the fuzzy inference system is very efficient and robust.The authors are grateful for the support from NSF ITR grant IIS-0428420 and the U.S. Administration on Aging, under grant 90AM3013

Recognizing Falls from Silhouettes

A major problem among the elderly involves falling. The recognition of falls from video first requires the segmentation of the individual from the background. To ensure privacy, segmentation should result in a silhouette that is a binary map indicating only the body position of the individual in an image. We have previously demonstrated a segmentation method based on color that can recognize the silhouette and detect and remove shadows. After the silhouettes are obtained, we extract features and train hidden Markov models to recognize future performances of these known activities. In this paper, we present preliminary results that demonstrate the usefulness of this approach for distinguishing between a few common activities, specifically with fall detection in mind.The authors were partially supported by NSF ITR grant IIS-0428420 and the U.S. Administration on Aging, under grant 90AM3013

Technology for Successful Aging

With our partners at the University of Virginia we are developing a system of sensors, to monitor the activity of seniors in their residences. We measure motion, footfalls, sleep and restlessness, we have stove sensors and sensing mats, all connected wirelessly to a computer which performs an initial evaluation and data transfer to a secure server for further study. Based upon the monitor data we will implement an intervention to ameliorate functional decline. Focus group studies determine the attitudes, concerns and impressions of the residents and staff. We find that senior's attitude to technology is healthy and they will try helpful approaches. In addition to the statistical comparisons, we model the data using hidden Markov models, integrate or fuse the monitor data with video images, and reason about behavior using fuzzy logic. The results of this work will additionally reduce the workload on caregivers, foster communication between residents and family,and give these seniors independence.The authors are grateful for the support from NSF ITR grant IIS-0428420 and the U.S. Administration on Aging, under grant 90AM3013

Fluid Particle Accelerations in Fully Developed Turbulence

The motion of fluid particles as they are pushed along erratic trajectories by fluctuating pressure gradients is fundamental to transport and mixing in turbulence. It is essential in cloud formation and atmospheric transport, processes in stirred chemical reactors and combustion systems, and in the industrial production of nanoparticles. The perspective of particle trajectories has been used successfully to describe mixing and transport in turbulence, but issues of fundamental importance remain unresolved. One such issue is the Heisenberg-Yaglom prediction of fluid particle accelerations, based on the 1941 scaling theory of Kolmogorov (K41). Here we report acceleration measurements using a detector adapted from high-energy physics to track particles in a laboratory water flow at Reynolds numbers up to 63,000. We find that universal K41 scaling of the acceleration variance is attained at high Reynolds numbers. Our data show strong intermittency---particles are observed with accelerations of up to 1,500 times the acceleration of gravity (40 times the root mean square value). Finally, we find that accelerations manifest the anisotropy of the large scale flow at all Reynolds numbers studied.Comment: 7 pages, 4 figure