Abstract
Audio/visual recognition and retrieval applications have recently garnered significant attention within Internetof-Things (IoT) oriented services, given that video cameras and audio processing chipsets are now ubiquitous even in low-end embedded systems. In the most typical scenario for such services, each device extracts audio/visual features and compacts them into feature descriptors, which comprise media queries. These queries are uploaded to a remote cloud computing service that performs content matching for classification or retrieval applications. Two of the most crucial aspects for such services are: (i) controlling the device energy consumption when using the service; (ii) reducing the billing cost incurred from the cloud infrastructure provider. In this paper we derive analytic conditions for the optimal coupling between the device energy consumption and the incurred cloud infrastructure billing. Our framework encapsulates: the energy consumption to produce and transmit audio/visual queries, the billing rates of the cloud infrastructure, the number of devices concurrently connected to the same cloud server, and the statistics of the query data production volume per device. Our analytic results are validated via a deployment with: (i) the device side comprising compact image descriptors (queries) computed on Beaglebone Linux embedded platforms and transmitted to Amazon Web Services (AWS) Simple Storage Service; (ii) the cloud side carrying out image similarity detection via AWS Elastic Compute Cloud (EC2) spot instances, with the AWS Auto Scaling being used to control the number of instances according to the demand. © 2016 IEEE.

Abstract

Abstract

Abstract
This Letter reports a new method for the generation of optical vortices using a micropatterned optical fiber tip. Here, a spiral phase plate (2 pi phase shift) is micromachined on the tip of an optical fiber using a focused ion beam. This is a high resolution method that allows milling the fibers with nanoscale resolution. The plate acts as a beam tailoring system, transforming the fundamental guided mode, specifically a Gaussian mode, into the Laguerre-Gaussian mode (LG(01)), which carries orbital angular momentum. The experimental results are supported by computational simulations based on the finite-difference time-domain method. (C) 2016 Optical Society of America

Abstract
Audio/visual recognition and retrieval applications have recently garnered significant attention within Internet-of-Things-oriented services, given that video cameras and audio processing chipsets are now ubiquitous even in low-end embedded systems. In the most typical scenario for such services, each device extracts audio/visual features and compacts them into feature descriptors, which comprise media queries. These queries are uploaded to a remote cloud computing service that performs content matching for classification or retrieval applications. Two of the most crucial aspects for such services are: 1) controlling the device energy consumption when using the service, and 2) reducing the billing cost incurred from the cloud infrastructure provider. In this paper, we derive analytic conditions for the optimal coupling between the device energy consumption and the incurred cloud infrastructure billing. Our framework encapsulates: the energy consumption to produce and transmit audio/visual queries, the billing rates of the cloud infrastructure, the number of devices concurrently connected to the same cloud server, the query volume constraint of each cluster of devices, and the statistics of the query data production volume per device. Our analytic results are validated via a deployment with: 1) the device side comprising compact image descriptors (queries) computed on Beaglebone Linux embedded platforms and transmitted to Amazon Web Services (AWS) Simple Storage Service, and 2) the cloud side carrying out image similarity detection via AWS Elastic Compute Cloud (EC2) instances, with the AWS Auto Scaling being used to control the number of instances according to the demand. © 2016 IEEE.

Abstract
Today, manufacturing is moving towards customer-driven and knowledge-based proactive production. Shorter product life cycles lead to increased complexity in areas such as product and process design, factory deployment and production operations. To handle this complexity, new knowledge-based methods and technologies are needed to model, simulate, optimize and monitor manufacturing systems. Existing large Enterprise Information Systems (EIS) impose structured and predictable workflow, while processes "on the ground" are often unpredictable and involve a large number of human based decisions and collaboration. This is leading to a major shift on EIS paradigm and leading to development of a set of specialized small applications, each one with fewer features, but highly specialized, flexible, cross linked and easy to use. This paper presents a hybrid management solution intended to support collaboration and decision in the scope of automotive engineering and planning. The solution, labelled as HPM - Hybrid Process Manager, encompasses a set of tools for work, information and communication management fully integrated with knowledge based engineering processes. Its overall aim is to ease the flow of information between all the partners, making it more reliable and actual, allowing a closer control and faster reaction to upcoming events. The adoption of HPM approach proves to be quite effective and efficient, leading to significant results in terms of cost and time saving. When using the solution, managers no longer need to constantly ask for reporting, leading to a significant reduction on email and paperwork. It is relevant to underline that the proposed approach allowed planners to concentrate in important issues improving the product and avoid non-value added efforts and time on collateral activities. Another main advantage stays on the experience retrieval module built in top of the solution, allowing easy access to expertise, knowledge and best practices generated by previous projects, so that they can be readily incorporated in the design of new processes as a factor of knowledge sustainability. (C) 2016 Published by Elsevier B.V.