The Impact of Parameter Identification Methods on Drug Therapy Control in an Intensive Care Unit

This paper investigates the impact of fast parameter identification methods, which do not require any forward simulations, on model-based glucose control, using retrospective data in the Christchurch Hospital Intensive Care Unit. The integral-based identification method has been previously clinically validated and extensively applied in a number of biomedical applications; and is a crucial element in the presented model-based therapeutics approach. Common non-linear regression and gradient descent approaches are too computationally intense and not suitable for the glucose control applications presented. The main focus in this paper is on better characterizing and understanding the importance of the integral in the formulation and the effect it has on model-based drug therapy control. As a comparison, a potentially more natural derivative formulation which has the same computation speed advantages is investigated, and is shown to go unstable with respect to modelling error which is always present clinically. The integral method remains robust

Active Object Search Exploiting Probabilistic Object–Object Relations

\u3cp\u3eThis paper proposes a probabilistic object-object relation based approach for an active object search. An important role of mobile robots will be to perform object-related tasks and active object search strategies deal with the non-trivial task of finding an object in unstructured and dynamically changing environments. This work builds further upon an existing approach exploiting probabilistic object-room relations for selecting the room in which an object is expected to be. Learnt object-object relations allow to search for objects inside a room via a chain of intermediate objects. Simulations have been performed to investigate the effect of the camera quality on path length and failure rate. Furthermore, a comparison is made with a benchmark algorithm based the same prior knowledge but without using a chain of intermediate objects. An experiment shows the potential of the proposed approach on the AMIGO robot.\u3c/p\u3

Face recognition : implementation of face recognition on AMIGO

In this (traineeship)report two possible methods of face recognition were presented. The first method describes how to detect and recognize faces by using the SURF algorithm. This algorithm finally was not used for recognizing faces, with the reason that the Eigenface algorithm was an already tested and proven method for facial recognition, which did not hold for the SURF method. This does not mean that SURF would not provide good results for face recognition. From the test data it was clear that SURF had some good results, but as mentioned before these results did not always have the same quality. The second method was the Eigenface method. Because this method proved to be capable of recognizing faces, it was implemented on AMIGO. The algorithm was used during the RoboCup@home challenges in Germany. It was capable of acquiring training faces, which was the first important step. After this acquiring step, due to a combination of an error in the world model and face detector, the algorithm ended up in an endless loop. Further results of the actual implantation on AMIGO are therefore still unknown

Semi-task-dependent and uncertainty-driven world model maintenance

\u3cp\u3eNearly every task a domestic robot could potentially solve requires a description of the robot’s environment which we call a world model. One problem underexposed in the literature is the maintenance of world models. Rather than on creating a world model, this work focuses on finding a strategy that determines when to update which object in the world model. The decision whether or not to update an object is based on the expected information gain obtained by the update, the action cost of the update and the task the robot performs. The proposed strategy is validated during both simulations and real world experiments. The extended series of simulations is performed to show both the performance gain with respect to a benchmark strategy and the effect of the various parameters. The experiments show the proposed approach on different set-ups and in different environments.\u3c/p\u3

Applying V2V for operational safety within cooperative adaptive cruise control

Cooperative Adaptive Cruise Control aims to automate a truck longitudinally for following its predecessor at reduced following distances in order to minimize fuel consumption. Short inter vehicle distances can be realised by the use of Vehicle-To-Vehicle communication (V2V). This application should be operational safe, which means to prevent harm to personnel in hazardous situations in case the system is fully operational: the system should avoid collisions with other road participants and with the leading truck. This paper proposes to use V2V communication in a platoon to share information on surrounding traffic participants in order to predict possible hazardous traffic situations continuously, which could be used to ensure functional safety in case of V2V failure. In case these situations can be predicted in time, actions could be taken to avoid collisions. \u3cbr/\u3

Active object search exploiting probabilistic object-object relations

\u3cp\u3eThis paper proposes a probabilistic object-object relation based approach for an active object search. An important role of mobile robots will be to perform object-related tasks and active object search strategies deal with the non-trivial task of finding an object in unstructured and dynamically changing environments. This work builds further upon an existing approach exploiting probabilistic object-room relations for selecting the room in which an object is expected to be. Learnt object-object relations allow to search for objects inside a room via a chain of intermediate objects. Simulations have been performed to investigate the effect of the camera quality on path length and failure rate. Furthermore, a comparison is made with a benchmark algorithm based the same prior knowledge but without using a chain of intermediate objects. An experiment shows the potential of the proposed approach on the AMIGO robot.\u3c/p\u3

Some Key Things U.S. Entrepreneurs Need to Know About the Law and Lawyers

New business formation is a powerful economic engine that creates jobs. Diverse legal issues are encountered as a start-up entity approaches formation, initial capitalization and fundraising, arrangements with employees and independent contractors, and relationships with other third parties. The endeavors of a typical start-up in the United States will likely implicate many of the following areas of law: intellectual property; business organizations; tax laws; employment and labor laws; securities regulation; contracts and licensing agreements; commercial sales; debtor-creditor relations; real estate law; health and safety laws/codes; permits and licenses; environmental protection; industry specific regulatory laws and approval processes; tort/personal injury, products liability, and insurance laws; antitrust and other unfair competition laws; import/export laws; immigration laws; laws related to the internet, privacy and e-commerce; and possibly many other federal, state and/or local laws, and, for many businesses these days, international laws. Company founders need to develop familiarity with the effects of such laws and need to access qualified legal talent to address legal issues in the planning and implementation of their venture. This article is designed to provide entrepreneurs with an overview of several areas of law that commonly arise in for-profit start-up ventures and offer them some important tips on working with lawyers