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This paper describes an early lumping approach for generating a mathematical model of the heating process of a moving dual-layer substrate. The heat is supplied by convection and nonlinearly distributed over the whole considered spatial extend of the substrate. Using CFD simulations as a reference, two different modelling approaches have been investigated in order to achieve the most suitable model type. It is shown that due to the possibility of using the transition matrix for time discretization, an equivalent circuit model achieves superior results when compared to the Crank-Nicolson method. In order to maintain a constant sampling time for the in-visioned-control strategies, the effect of variable speed is transformed into a system description, where the state vector has constant length but a variable number of non-zero entries. The handling of the variable transport speed during the heating process is considered as the main contribution of this work. The result is a model, suitable for being used in future control strategies.
Online-based business models, such as shopping platforms, have added new possibilities for consumers over the last two decades. Aside from basic differences to other distribution channels, customer reviews on such platforms have become a powerful tool, which bestows an additional source for gaining transparency to consumers. Related research has, for the most part, been labelled under the term electronic word-of-mouth (eWOM). An approach, providing a theoretical basis for this phenomenon, will be provided here. The approach is mainly based on work in the field of consumer culture theory (CCT) and on the concept of co-creation. The work of several authors in these streams of research is used to construct a culturally informed resource-based theory, as advocated by Arnould & Thompson and Algesheimer & Gurâu.
This contribution presents a data compression scheme for applications in non-volatile flash memories. The objective of the data compression algorithm is to reduce the amount of user data such that the redundancy of the error correction coding can be increased in order to improve the reliability of the data storage system. The data compression is performed on block level considering data blocks of 1 kilobyte. We present an encoder architecture that has low memory requirements and provides a fast data encoding.
This work proposes a decoder implementation for high-rate generalized concatenated (GC) codes. The proposed codes are well suited for error correction in flash memories for high reliability data storage. The GC codes are constructed from inner extended binary Bose-Chaudhuri-Hocquenghem (BCH) codes and outer Reed-Solomon (RS) codes. The extended BCH codes enable high-rate GC codes. Moreover, the decoder can take advantage of soft information. For the first three levels of inner codes we propose an optional Chase soft decoder. In this work, the code construction is explained and a decoder architecture is presented. Furthermore, area and throughput results are discussed.
This paper presents the implementation of deep learning methods for sleep stage detection by using three signals that can be measured in a non-invasive way: heartbeat signal, respiratory signal, and movement signal. Since signals are measurements taken during the time, the problem is seen as time-series data classification. Deep learning methods are chosen to solve the problem are convolutional neural network and long-short term memory network. Input data is structured as a time-series sequence of mentioned signals that represent 30 seconds epoch, which is a standard interval for sleep analysis. The records used belong to the overall 23 subjects, which are divided into two subsets. Records from 18 subjects were used for training the data and from 5 subjects for testing the data. For detecting four sleep stages: REM (Rapid Eye Movement), Wake, Light sleep (Stage 1 and Stage 2), and Deep sleep (Stage 3 and Stage 4), the accuracy of the model is 55%, and F1 score is 44%. For five stages: REM, Stage 1, Stage 2, Deep sleep (Stage 3 and 4), and Wake, the model gives an accuracy of 40% and F1 score of 37%.
Modeling a suitable birth density is a challenge when using Bernoulli filters such as the Labeled Multi-Bernoulli (LMB) filter. The birth density of newborn targets is unknown in most applications, but must be given as a prior to the filter. Usually the birth density stays unchanged or is designed based on the measurements from previous time steps.
In this paper, we assume that the true initial state of new objects is normally distributed. The expected value and covariance of the underlying density are unknown parameters. Using the estimated multi-object state of the LMB and the Rauch-Tung-Striebel (RTS) recursion, these parameters are recursively estimated and adapted after a target is detected.
The main contribution of this paper is an algorithm to estimate the parameters of the birth density and its integration into the LMB framework. Monte Carlo simulations are used to evaluate the detection driven adaptive birth density in two scenarios. The approach can also be applied to filters that are able to estimate trajectories.
Error correction coding (ECC) for optical communication and persistent storage systems require high rate codes that enable high data throughput and low residual errors. Recently, different concatenated coding schemes were proposed that are based on binary Bose-Chaudhuri-Hocquenghem (BCH) codes that have low error correcting capabilities. Commonly, hardware implementations for BCH decoding are based on the Berlekamp-Massey algorithm (BMA). However, for single, double, and triple error correcting BCH codes, Peterson's algorithm can be more efficient than the BMA. The known hardware architectures of Peterson's algorithm require Galois field inversion. This inversion dominates the hardware complexity and limits the decoding speed. This work proposes an inversion-less version of Peterson's algorithm. Moreover, a decoding architecture is presented that is faster than decoders that employ inversion or the fully parallel BMA at a comparable circuit size.
The digital transformation of business processes and the integration of IT systems leads to opportunities and risks for small and medium-sized enterprises (SMEs). Risks that can result in a lack of IT Governance, Risk and Compliance (IT-GRC). The purpose of this paper is to present the current state of the research project. With this, the Design Science Research approach based on Hevner is using. Based on the phase of Problem Identification and Objectives, this paper will deal with the development of an artefact and thus present the draft of the Design phase. The artefact will be developed by selecting relevant existing frameworks and standards and the identification of SME-specific conditions.
Twenty-first century infrastructure needs to respond to changing demographics, becoming climate neutral, resilient and economically affordable, while remaining a driver for development and shared prosperity. However, the infrastructure sector remains one of the least innovative and digitalised, plagued by delays, cost overruns and benefit shortfalls (Cantarelli et al. 2008; Flyvbjerg, 2007; Flyvbjerg et al., 2003; Flyvbjerg et al., 2004). The root cause is the prevailing fragmentation of the infrastructure sector (Fellows and Liu, 2012). To help overcome these challenges, integration of the value chain is needed. This could be achieved through a use-case-based creation of federated ecosystems connecting open and trusted data spaces and advanced services applied to infrastructure projects. Such digital platforms enable full-lifecycle participation and responsible governance guided by a shared infrastructure vision. Digital federation enables secure and sovereign data exchange and thus collaboration across the silos within the infrastructure sector and between industries as well as within and between countries. Such an approach to infrastructure technology policy would not rely on technological solutionism but proposes the development of open and trusted data alliances. Federated data spaces provide access to the emerging data economy, especially for SMEs, and can foster the innovation of new digital services. Such responsible digital governance can help make the infrastructure sector more resilient, efficient and aligned with the realisation of ambitious decarbonisation and environmental protection targets. The European Union and the United States have already developed architectures for sovereign and secure data exchange.