Sustainable packaging in the healthcare industry

The recycling of plastics tends to lag behind other packaging materials. The research investigates opportunities to improve the capture of valuable packaging polymers and to preserve their specification during recycle operations, thus increasing second user opportunity. The legislative and policy drivers on the sustainable use of plastics are described and discussed with particular reference to achieving sustainability, reuse and recycle of healthcare packaging materials. Four strategic methods of achieving improvements in sustainability, reuse and recycle are developed to represent aspects of sorting of materials, collection of recyclables, replacement of unsustainable packaging materials and measurement of the environmental impacts of packaging and changes in packaging, using examples of packaging from GlaxoSmithKline consumer healthcare and medical products. The use of radio frequency identification methodology as a means of separating high quality plastics and individual reusable devices from mixed waste streams has been developed and trialled under simulated materials recycling and separation conditions. The use of Reverse Vending Machines (RVM's) designed to capture high quality polyethylene terephthalate polymers is described along with results of successful trials on this method of capture in the out of home consumption sector. Recovered material is suitable for reuse in food grade applications after reprocessing. A novel biodegradable packaging material has been successfully developed from sources of green waste as an alternative to existing polymer packaging materials for transport of vaccines, and provides results that are extendable to the replacement of other types of packaging over a wide range of consumer goods. The material also offers intangible benefits to a business in terms of claims that can be made within a corporate social responsibility (CSR) report. Life cycle analysis methodologies have been used to illustrate the environmental benefits that can be achieved by reuse of polypropylene as an example of a widely used packaging polymer with potential for reuse in other industrial sectors. The implications of the results obtained in this work should be of value in the future eco-design of polymer products designed to make end-of-life recovery and recycle more efficient and environmentally beneficial

Methods to improve the reliability and resiliency of near/sub-threshold digital circuits

Energy consumption is one of the primary bottlenecks to both large and small scale modern compute platforms. Reducing the operating voltage of digital circuits to voltages where the supply voltage is near or below the threshold of the transistors has recently gained attention as a method to reduce the energy required for computations by as much as 6 times. However, when operating at near/sub-threshold voltages (where the supply voltage is near or below the threshold of the transistors), imperfections in transistor manufacturing, changes in temperature, and other difficult-to-predict factors cause wide variations in the timing of Complementary Metal-Oxide Semiconductor (CMOS) circuits due to an increased sensitivity at lower voltages. These increased variations result in poor aggregate performance and cause increased rates of error occurrence in computation. This work introduces several new methods to improve the reliability of near/sub-threshold circuits. The first is a design automation technique that is used to aid in low-voltage digital standard cell synthesis. Second, two circuit-level techniques are also introduced that aim to improve the reliability and resiliency of digital circuits by means of completion/error detection. These techniques are shown to improve speed and lower energy consumption at low overheads compared to previous methods. Most importantly, these circuit-level methods are specifically designed to operate at low voltages and can themselves tolerate variations and operation in harsh environments. Finally, a test-chip prototype designed in 65nm-CMOS demonstrates the practicality and feasibility of a proposed current sensing error detector

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Collective Communications and Computation Mechanisms on the RF Channel for Organic Printed Smart Labels and Resource-limited IoT Nodes

Radio Frequency IDentification (RFID) and Wireless Sensor Networks (WSN) are seen as enabler technologies for realizing the Internet of Things (IoT). Organic and printed Electronics (OE) has the potential to provide low cost and all-printable smart RFID labels in high volumes. With regard to WSN, power harvesting techniques and resource-efficient communications are promising key technologies to create sustainable and for the environment friendly sensing devices. However, the implementation of OE smart labels is only allowing printable devices of ultra-low hardware complexity, that cannot employ standard RFID communications. And, the deployment of current WSN technology is far away from offering battery-free and low-cost sensing technology. To this end, the steady growth of IoT is increasing the demand for more network capacity and computational power. With respect to wireless communications research, the state-of-the-art employs superimposed radio transmission in form of physical layer network coding and computation over the MAC to increase information flow and computational power, but lacks on practicability and robustness so far. With regard to these research challenges we developed in particular two approaches, i.e., code-based Collective Communications for dense sensing environments, and time-based Collective Communications (CC) for resource-limited WSNs. In respect to the code-based CC approach we exploit the principle of superimposed radio transmission to acquire highly scalable and robust communications obtaining with it at the same time as well minimalistic smart RFID labels, that can be manufactured in high volume with present-day OE. The implementation of our code-based CC relies on collaborative and simultaneous transmission of randomly drawn burst sequences encoding the data. Based on the framework of hyper-dimensional computing, statistical laws and the superposition principle of radio waves we obtained the communication of so called ensemble information, meaning the concurrent bulk reading of sensed values, ranges, quality rating, identifiers (IDs), and so on. With 21 transducers on a small-scale reader platform we tested the performance of our approach successfully proving the scalability and reliability. To this end, we implemented our code-based CC mechanism into an all-printable passive RFID label down to the logic gate level, indicating a circuit complexity of about 500 transistors. In respect to time-based CC approach we utilize the superimposed radio transmission to obtain resource-limited WSNs, that can be deployed in wide areas for establishing, e.g., smart environments. In our application scenario for resource-limited WSN, we utilize the superimposed radio transmission to calculate functions of interest, i.e., to accomplish data processing directly on the radio channel. To prove our concept in a case study, we created a WSN with 15 simple nodes measuring the environmental mean temperature. Based on our analysis about the wireless computation error we were able to minimize the stochastic error arbitrarily, and to remove the systematic error completely