Quantized Detector Networks: A review of recent developments

QDN (quantized detector networks) is a description of quantum processes in which the principal focus is on observers and their apparatus, rather than on states of SUOs (systems under observation). It is a realization of Heisenberg's original instrumentalist approach to quantum physics and can deal with time dependent apparatus, multiple observers and inter-frame physics. QDN is most naturally expressed in the mathematical language of quantum computation, a language ideally suited to describe quantum experiments as processes of information exchange between observers and their apparatus. Examples in quantum optics are given, showing how the formalism deals with quantum interference, non-locality and entanglement. Particle decays, relativity and non-linearity in quantum mechanics are discussed.Comment: 59 pages, 14 figures, to be published in Int. J. Mod. Phys.

Futures Studies in the Interactive Society

This book consists of papers which were prepared within the framework of the research project (No. T 048539) entitled Futures Studies in the Interactive Society (project leader: Éva Hideg) and funded by the Hungarian Scientific Research Fund (OTKA) between 2005 and 2009. Some discuss the theoretical and methodological questions of futures studies and foresight; others present new approaches to or procedures of certain questions which are very important and topical from the perspective of forecast and foresight practice. Each study was conducted in pursuit of improvement in futures fields

Establishment of surface functionalization methods for spore-based biosensors and implementation into sensor technologies for aseptic food processing

Aseptic processing has become a popular technology to increase the shelf-life of packaged products and to provide non-contaminated goods to the consumers. In 2017, the global aseptic market was evaluated to be about 39.5 billion USD. Many liquid food products, like juice or milk, are delivered to customers every day by employing aseptic filling machines. They can operate around 12,000 ready-packaged products per hour (e.g., Pure-Pak® Aseptic Filling Line E-PS120A). However, they need to be routinely validated to guarantee contamination-free goods. The state-of-the-art methods to validate such machines are by means of microbiological analyses, where bacterial spores are used as test organisms because of their high resistance against several sterilants (e.g., gaseous hydrogen peroxide). The main disadvantage of the aforementioned tests is time: it takes at least 36-48 hours to get the results, i.e., the products cannot be delivered to customers without the validation certificate. Just in this example, in 36 hours, 432,000 products would be on hold for dispatchment; if more machines are evaluated, this number would linearly grow and at the end, the costs (only for waiting for the results) would be considerably high. For this reason, it is very valuable to develop new sensor technologies to overcome this issue. Therefore, the main focus of this thesis is on the further development of a spore-based biosensor; this sensor can determine the viability of spores after being sterilized with hydrogen peroxide. However, the immobilization strategy as well as its implementation on sensing elements and a more detailed investigation regarding its operating principle are missing. In this thesis, an immobilization strategy is developed to withstand harsh conditions (high temperatures, oxidizing environment) for spore-based biosensors applied in aseptic processing. A systematic investigation of the surface functionalization’s effect (e.g., hydroxylation) on sensors (e.g., electrolyte-insulator semiconductor (EIS) chips) is presented. Later on, organosilanes are analyzed for the immobilization of bacterial spores on different sensor surfaces. The electrical properties of the immobilization layer are studied as well as its resistance to a sterilization process with gaseous hydrogen peroxide. In addition, a sensor array consisting of a calorimetric gas sensor and a spore-based biosensor to measure hydrogen peroxide concentrations and the spores’ viability at the same time is proposed to evaluate the efficacy of sterilization processes

Microcomputer Intelligence for Technical Training (MITT): The evolution of an intelligent tutoring system

Microcomputer Intelligence for Technical Training (MITT) uses Intelligent Tutoring System (OTS) technology to deliver diagnostic training in a variety of complex technical domains. Over the past six years, MITT technology has been used to develop training systems for nuclear power plant diesel generator diagnosis, Space Shuttle fuel cell diagnosis, and message processing diagnosis for the Minuteman missile. Presented here is an overview of the MITT system, describing the evolution of the MITT software and the benefits of using the MITT system

On the application of reservoir computing networks for noisy image recognition

Reservoir Computing Networks (RCNs) are a special type of single layer recurrent neural networks, in which the input and the recurrent connections are randomly generated and only the output weights are trained. Besides the ability to process temporal information, the key points of RCN are easy training and robustness against noise. Recently, we introduced a simple strategy to tune the parameters of RCNs. Evaluation in the domain of noise robust speech recognition proved that this method was effective. The aim of this work is to extend that study to the field of image processing, by showing that the proposed parameter tuning procedure is equally valid in the field of image processing and conforming that RCNs are apt at temporal modeling and are robust with respect to noise. In particular, we investigate the potential of RCNs in achieving competitive performance on the well-known MNIST dataset by following the aforementioned parameter optimizing strategy. Moreover, we achieve good noise robust recognition by utilizing such a network to denoise images and supplying them to a recognizer that is solely trained on clean images. The experiments demonstrate that the proposed RCN-based handwritten digit recognizer achieves an error rate of 0.81 percent on the clean test data of the MNIST benchmark and that the proposed RCN-based denoiser can effectively reduce the error rate on the various types of noise. (c) 2017 Elsevier B.V. All rights reserved

Talking about routines in the field: the emergence of organizational capabilities in a new cellular phone network company

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