Queuing theoretic and information theoretic capacity of energy harvesting sensor nodes

Energy harvesting sensor networks provide near perpetual operation and reduce carbon emissions thereby supporting `green communication'. We study such a sensor node powered with an energy harvesting source. We obtain energy management policies that are throughput optimal. We also obtain delay-optimal policies. Next we obtain the Shannon capacity of such a system. Further we combine the information theoretic and queuing theoretic approaches to obtain the Shannon capacity of an energy harvesting sensor node with a data queue. Then we generalize these results to models with fading and energy consumption in activities other than transmission.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

Essex Adult Community Learning: PTLLS for distance and blended learning

Funded by the LSIS Excellence and Improvement Fund, Essex Adult Community Learning has produced a distance/blended learning resource on CD-Rom for tutors in the post-16 sector to achieve the City & Guilds PTLLS (7303 Preparing to Teach in the Lifelong Learning Sector) at Level 4. The aim of the resource is to improve teaching and learning by providing a flexible way to access initial teacher training where candidates may otherwise find it difficult or impossible to attend a taught course. It is also intended to increase tutors' own e-learning skills.

Good practice guidance for library and information professionals - moving towards accessible e-book platforms

Good practice guidance for publishers and procurers of e-book platforms based on the benefits and barriers identified by robust testing using real people with disabilities

Contributions to unsupervised and supervised learning with applications in digital image processing

311 p. : il.

Report of the Sea Turtle Health Assessment Workshop, 2-3 February 1998, Part I: Background and information needs

A two day workshop was convened on February 2-3, 1998 in Charleston, SC with 20 invited experts in various areas of sea turtle research. The goal of this workshop was to review current information on sea turtles with repect to health and identify data gaps. The use of a suite of health assessment indicators will provide insight on the health status of sea turtle populations. Since the relationship of health factors of sea turtles is limited, a seconde workshop was planned. Using a tiered approach, the first workshop we identified and reviewed the available, pertinent baseline information and data gaps. The second workshop will focus on developing the framework for the research plan. The workshops will address the use of integrated set of health parameters; specific objectives are: 1) Identify reliable indicators of health in sea turtles: assess advantages and disadvantages; determine new indicators/biomarkers which may be useful; 2) Review existing sea turtle field sampling projects; 3) Identify field projects suitable for inclusion for health assessment sampling; 4) Identify data gaps, particularly environmental characterization; 5) Identify new health assessment sampling sites, including reference site(s); and 6) Develop integrated five-year research plan, with focus on health assessment of environmental characterization. (PDF contains 174 pages)