Shape and Trajectory Tracking of Moving Obstacles

This work presents new methods and algorithms for tracking the shape and trajectory of moving reflecting obstacles with broken rays, or rays reflecting at an obstacle. While in tomography the focus of the reconstruction method is to recover the velocity structure of the domain, the shape and trajectory reconstruction procedure directly finds the shape and trajectory of the obstacle. The physical signal carrier for this innovative method are ultrasonic beams. When the speed of sound is constant, the rays are straight line segments and the shape and trajectory of moving objects will be reconstructed with methods based on the travel time equation and ellipsoid geometry. For variable speed of sound, we start with the eikonal equation and a system of differential equations that has its origins in acoustics and seismology. In this case, the rays are curves that are not necessarily straight line segments and we develop algorithms for shape and trajectory tracking based on the numerical solution of these equations. We present methods and algorithms for shape and trajectory tracking of moving obstacles with reflected rays when the location of the receiver of the reflected ray is not known in advance. The shape and trajectory tracking method is very efficient because it is not necessary for the reflected signal to traverse the whole domain or the same path back to the transmitter. It could be received close to the point of reflection or far away from the transmitter. This optimizes the energy spent by transmitters for tracking the object, reduces signal attenuation and improves image resolution. It is a safe and secure method. We also present algorithms for tracking the shape and trajectory of absorbing obstacles. The new methods and algorithms for shape and trajectory tracking enable new applications and an application to one-hop Internet routing is presented.Comment: 22 pages, 2 figures, 2 table

To what extent is the taking and use of neuroscientific evidence compatible with the rights enshrined in the European Convention of Human Rights?

Criminal law is arguably among the most important parts of any system of law as its purpose is to counter serious forms of socially undesired behavior such as assault on one’s property, physical integrity and life. Therefore in order to fulfill the high expectations society has of it, it should be equipped with the best tools to find out the truth, determine who is guilty and either punish or send them to rehabilitation. To achieve these goals it has the power to gather all kinds of evidence and invade people’s liberties and private lives. These extensive prerogatives are controlled to a large extent, but not only, by the protection from the state’s intrusion into private life enshrined in human rights documents such as the right to fair trial and the right to privacy in the European Convention on Human Rights (ECHR). The protection granted to individuals in this way, however, is far from absolute and it is often reduced in the process of solving particularly important cases. Thus the answer to whether human rights are infringed can often be ambiguous, policy-driven and depend on the balancing of interests in the particular case.This paper endeavors to discuss whether the compulsory taking and use of neuroscientific evidence in the form of fMRI lie-detection and Guilty Knowledge Tests (‘GKT’ from now on) as well as Brain Fingerprinting (‘BF’ from now on) detection of existing knowledge from the defendant in criminal proceedings complies with the right to fair trial and particularly the right to silence which is part of it; and the right to privacy as enshrined in the European Convention on Human Rights.   

Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline.

Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges--management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu