Random Access in DVB-RCS2: Design and Dynamic Control for Congestion Avoidance

In the current DVB generation, satellite terminals are expected to be interactive and capable of transmission in the return channel with satisfying quality. Considering the bursty nature of their traffic and the long propagation delay, the use of a random access technique is a viable solution for such a Medium Access Control (MAC) scenario. In this paper, random access communication design in DVB-RCS2 is considered with particular regard to the recently introduced Contention Resolution Diversity Slotted Aloha (CRDSA) technique. This paper presents a model for design and tackles some issues on performance evaluation of the system by giving intuitive and effective tools. Moreover, dynamic control procedures that are able to avoid congestion at the gateway are introduced. Results show the advantages brought by CRDSA to DVB-RCS2 with regard to the previous state of the art.Comment: Accepted for publication: IEEE Transactions on Broadcasting; IEEE Transactions on Broadcasting, 201

A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness of the proposed controller over the first order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on a DC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desired transient response without causing chattering and error under steady-state conditions. The proposed controller is able to give robust performance in terms of rejection to input voltage variations and load variations.Comment: 14 page

Exploring personalized life cycle policies

Ambient Intelligence imposes many challenges in protecting people's privacy. Storing privacy-sensitive data permanently will inevitably result in privacy violations. Limited retention techniques might prove useful in order to limit the risks of unwanted and irreversible disclosure of privacy-sensitive data. To overcome the rigidness of simple limited retention policies, Life-Cycle policies more precisely describe when and how data could be first degraded and finally be destroyed. This allows users themselves to determine an adequate compromise between privacy and data retention. However, implementing and enforcing these policies is a difficult problem. Traditional databases are not designed or optimized for deleting data. In this report, we recall the formerly introduced life cycle policy model and the already developed techniques for handling a single collective policy for all data in a relational database management system. We identify the problems raised by loosening this single policy constraint and propose preliminary techniques for concurrently handling multiple policies in one data store. The main technical consequence for the storage structure is, that when allowing multiple policies, the degradation order of tuples will not always be equal to the insert order anymore. Apart from the technical aspects, we show that personalizing the policies introduces some inference breaches which have to be further investigated. To make such an investigation possible, we introduce a metric for privacy, which enables the possibility to compare the provided amount of privacy with the amount of privacy required by the policy

Open windrow composting of polymers: An investigation into the rate of degradation of polyethylene

The compostability of degradable polymers under open windrow composting conditions is explored within this paper. Areas for consideration were the use of, and impacts of, degradable polyethylene (PE) sacks on the composting process and the quality of the finished compost product. These factors were investigated through polymer weight loss over the composting process, the amount of polymer residue and chemical contaminants in the finished compost product, the windrow temperature profiles and a bioassay to establish plant growth and germination levels using the final compost product. This trial also included a comparative study of the weight loss under composting conditions of two different types of ‘degradable’ polymer sacks currently on the European market: PE and a starch based product. Statistical analysis of the windrow temperature profiles has led to the development of a model, which can help to predict the expected trends in the temperature profiles of open compost windrows where the organic waste is kerbside collected using a degradable PE sack

Municipal wastewater treatment and associated bioenergy generation using anaerobic granular bed baffled reactor

This study assesses a modified anaerobic granular bed baffled reactor (GRABBR) which was assessed for municipal wastewater treatment at high organic loading rates (chemical oxygen demand ≥ 1,100 mg/l) under varying temperatures. For the two mesophilic temperatures tested (37⁰C and 25⁰C) under steady state conditions, the removal of Chemical OxygenDemand (COD) and Biochemical Oxygen Demand (BOD) was 80 to 90 %. At lower organic loadings, the reactor operated as a completely mixed system with most of the treatment occurring in the first two compartments. The GRABBR also showed very high solids retention with low effluent suspended solids concentration for all organic and hydraulic conditions. Applications ofGRABBR as a single unit, two-phase treatment system could be an economical option reducing the cost to achieve similar treatment goals for high strength wastewaters. The findings of this research suggest that the application of GRABBR is suitable for the treatment of multiple pollutants present in wastewater where each compartment acts as a specialised treatment stagewith biogas production

Unexpected Event Prediction in Wire Electrical Discharge Machining Using Deep Learning Techniques

Theoretical models of manufacturing processes provide a valuable insight into physical phenomena but their application to practical industrial situations is sometimes difficult. In the context of Industry 4.0, artificial intelligence techniques can provide efficient solutions to actual manufacturing problems when big data are available. Within the field of artificial intelligence, the use of deep learning is growing exponentially in solving many problems related to information and communication technologies (ICTs) but it still remains scarce or even rare in the field of manufacturing. In this work, deep learning is used to efficiently predict unexpected events in wire electrical discharge machining (WEDM), an advanced machining process largely used for aerospace components. The occurrence of an unexpected event, namely the change of thickness of the machined part, can be effectively predicted by recognizing hidden patterns from process signals. Based on WEDM experiments, different deep learning architectures were tested. By using a combination of a convolutional layer with gated recurrent units, thickness variation in the machined component could be predicted in 97.4% of cases, at least 2 mm in advance, which is extremely fast, acting before the process has degraded. New possibilities of deep learning for high-performance machine tools must be examined in the near future.The authors gratefully acknowledge the funding support received from the Spanish Ministry of Economy and Competitiveness and the FEDER operation program for funding the project "Scientific models and machine-tool advanced sensing techniques for efficient machining of precision components of Low Pressure Turbines" (DPI2017-82239-P) and UPV/EHU (UFI 11/29). The authors would also like to thank Euskampus and ONA-EDM for their support in this project