HTTP adaptive steaming (HAS) is becoming ubiquitous as a reliable method of delivering video content over the open Internet to a variety of devices fromBy introducing various network impairments, we were able to demonstrate that video quality gracefully declined until network conditions became too harsh. As a baseline, we compared them with the Microsoft Mediaroom* Internet Protocol television (IPTV) solution currently deployed by service providers. The HD content was encoded at 6.0 Mb/s.The second approach was to create several clips in which exaggerated errors (such as visual artifacts) typical of HAS were introduced on purpose. These were shown to a much wider audience than we could bring into our test labs. Specifically, we used 150 volunteers from the Alcatel-Lucent Youth Lab to offer their subjective assessment of several pieces of content.From these experiments, we conclude that HAS is suitable for delivery of quality video over unmanaged IntroductionHTTP adaptive steaming (HAS) is becoming ubiquitous as a reliable method of streaming video content over unmanaged networks to a sizable number of classes of devices including smartphones, tablets, laptops and desktop personal computers (PCs) connected to small and medium-sized screens. It achieves this by dynamically adjusting the video quality to match the available bandwidth. This paper reports on two approaches to end user subjective testing of response to these changes in quality.The first approach was to invite a panel of viewers into our test lab facility. They were asked to rate high definition (HD) video content displayed by commercially available HTTP adaptive streaming solutions including Microsoft Smooth Streaming, Apple HTTP Live Streaming, and Adobe Dynamic Streaming.

Time taken to complete a tele-operated task with a mobile-robot partly depends on how a human operator interacts with the mobile-robot. Current tele-operated systems tend to rely heavily on visual feedback and experienced operators and this paper investigates how to make their tasks easier using an expert system to interpret joystick and sensor data. Simple expert systems improve that interaction for a tele-operated mobile-robot using ultrasonic sensors. Systems identify potentially hazardous situations and recommend safe courses of action. Results are presented from a series of timed tasks completed by tele-operators using a joystick to control a mobile-robot via an umbilical cable and watching the robot while operating it or sitting at a computer and viewing the area ahead of the robot. Tele-operators completed tests both with and without sensors and using the recently published systems to compare results. The new systems described here consistently performed tasks more quickly than some recently published systems. The paper also suggests that the amount of sensor support should be varied depending on circumstances.

The effect on failure rates of the way tele-operators interact with mobile robots is investigated. Human tele-operators attempted to move a robot through progressively more complicated environments with reducing gaps, as quickly as possible. Tele-operators used a joystick and either watched robots, while operating them, or used a computer screen to view scenes remotely. Cameras were either mounted on the robot to view the space ahead of the robot or mounted remotely so that they viewed both the environment and robot. Tele-operators completed tests both with and without sensors. Both an umbilical cable and a radio link were used.

Compressed air systems are often the most expensive and inefficient industrial systems. For every 10 units of energy, less than 1 unit turns into useful compressed air. Air compressors tend to be kept fully on even if they are not (all) needed. The research proposed in this short paper will combine real time ambient sensing with Artificial Intelligence and Knowledge Management to automatically improve efficiency in energy intensive manufacturing. The research will minimise energy use for air compressors based on real-time manufacturing conditions (and anticipated future requirements). Ambient data will provide detailed information on performance. Artificial Intelligence will make sense of that data and automatically act. Knowledge Management will facilitate the processing of information to advise human operators on actions to reduce energy use and maintain productivity. The aim is to create new intelligent techniques to save energy in compressed air systems.

Purpose – This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Design/methodology/approach – Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Findings – The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support. Research limitations/implications – Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems. Practical implications – Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support. Social implications – This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making. Originality/value – This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined AmI and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.