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This paper proposes a soft input decoding algorithm and a decoder architecture for generalized concatenated (GC) codes. The GC codes are constructed from inner nested binary Bose-Chaudhuri-Hocquenghem (BCH) codes and outer Reed-Solomon codes. In order to enable soft input decoding for the inner BCH block codes, a sequential stack decoding algorithm is used. Ordinary stack decoding of binary block codes requires the complete trellis of the code. In this paper, a representation of the block codes based on the trellises of supercodes is proposed in order to reduce the memory requirements for the representation of the BCH codes. This enables an efficient hardware implementation. The results for the decoding performance of the overall GC code are presented. Furthermore, a hardware architecture of the GC decoder is proposed. The proposed decoder is well suited for applications that require very low residual error rates.
This paper considers intervals of real matrices with respect to partial orders and the problem to infer from some exposed matrices lying on the boundary of such an interval that all real matrices taken from the interval possess a certain property. In many cases such a property requires that the chosen matrices have an identically signed inverse. We also briefly survey related problems, e.g., the invariance of matrix properties under entry-wise perturbations.
Even though immutability is a desirable property, especially in a multi-threaded environment, implementing immutable Java classes is surprisingly hard because of a lack of language support. We present a static analysis tool using abstract bytecode interpretation that checks Java classes for compliance with a set of rules that together constitute state-based immutability. Being realized as a Find Bugs plug in, the tool can easily be integrated into most IDEs and hence the software development process. Our evaluation on a large, real world codebase shows that the average run-time effort for a single class is in the range of a few milliseconds, with only a very few statistical spikes.
Domain-Specific modelling is increasingly adopted in the software development industry. While textual domain specific languages (DSLs) already have a wide impact, graphical DSLs still need to live up to their full potential. In this paper we describe an approach that reduces the time to create a graphical DSL to hours instead of months. The paper describes a generative approach to the creation of graphical editors for the Eclipse platform. A set of carefully designed textual DSLs together with an EMF meta-model are the input for the generator. The output is an Eclipse plugin for a graphical editor for the intended graphical language. The entire project is made available as open source under the name Spray and is being developed by an active community. This paper focuses on the description of the workflow and provides an introduction into the possibilities through this approach of a graphical modelling environment.
To learn from the past, we analyse 1,088 "computer as a target" judgements for evidential reasoning by extracting four case elements: decision, intent, fact, and evidence. Analysing the decision element is essential for studying the scale of sentence severity for cross-jurisdictional comparisons. Examining the intent element can facilitate future risk assessment. Analysing the fact element can enhance an organization's capability of analysing criminal activities for future offender profiling. Examining the evidence used against a defendant from previous judgements can facilitate the preparation of evidence for upcoming legal disclosure. Follow the concepts of argumentation diagrams, we develop an automatic judgement summarizing system to enhance the accessibility of judgements and avoid repeating past mistakes. Inspired by the feasibility of extracting legal knowledge for argument construction and employing grounds of inadmissibility for probability assessment, we conduct evidential reasoning of kernel traces for forensic readiness. We integrate the narrative methods from attack graphs/languages for preventing confirmation bias, the argumentative methods from argumentation diagrams for constructing legal arguments, and the probabilistic methods from Bayesian networks for comparing hypotheses.
The corrosion resistance of stainless steels is massively influenced by the condition of their surface. The surface quality includes the topography of the surface, the structure and composition of the passive layer, and the surface near structure of the base material. These factors are influenced by final physical/chemical surface treatments. The presented work shows significantly lower corrosion resistance for mechanical machined specimens than for etched specimens. It also turns out that the rougher the surface, the lower the corrosion resistance gets. However, there is no general finding which shows if blasted or grinded surfaces are more appropriate, but a dependency on process parameters and the characteristics on corrosive exposure in terms of corrosion behavior. The results show that not only the surface roughness Ra has an influence on corrosion behavior but also the shape of peaks and valleys which are evolved by surface treatments. Imperfections in the base material, like sulfidic inclusions lead to a weaker passive layer, respectively, to a decrease of the corrosion resistance. By using special passivating techniques the corrosion resistance of stainless steels can be increased to a higher level in comparison to common passivation.
Domain-specific modeling is more and more understood as a comparable solution compared to classical software development. Textual domain-specific languages (DSLs) already have a massive impact in contrast tographical DSLs, they still have to show their full potential. The established textual DSLs are normally generated from a domain specific grammar or maybe other specific textual descriptions. And advantage of textual DSLs is that they can be development cost-efficient.
In this paper, we describe asimilar approach for the creation of graphical DSLs from textual descriptions. We present a set of specially developed textual DSLs to fully describe graphical DSLs based on node and edge diagrams. These are, together with an EMF meta-model, the input for a generator that produces an eclipse-based graphical Editor. The entire project is available as open source under the name MoDiGen.
Purpose The purpose of this paper is to find out tourism movement patterns via the tracking of tourists with the help of positioning systems like GPS in the rural area of the Lake Constance destination in Germany. In doing so past, present and future of tourist tracking is illustrated. Design/methodology/approach The tracking is realized via common smartphones extended by an app, with dedicated sensors like position loggers and a survey. The three different approaches are applied in order to compare and cross-check results (triangulation of data and methods). Findings Movement patterns turned out to be diverse and individualistic within the rural destination of Lake Constance and following an ants trail in sub-destinations like the city of Constance. Repeat visitors and first-time visitors alike always visit the bigger cities and main day-trip destinations of the Lake. A possible prediction tool enables new avenues of governing tourism movement patterns. Research limitations/implications The tracking techniques can be developed further into the direction of “quantified self” using gamification in order to make the tracking app even more attractive. Practical implications An algorithm-based prediction tool would offer new perspectives to the management of tourism movements. Social implications Further research is needed to overcome the feeling of invasiveness of the app to allow tracking with that approach. Originality/value This study is original and innovative because of the first-time use of a smartphone app in tourist tracking, the application on a rural destination and the conceptual description of a prediction tool.
Sleep is an important aspect in life of every human being. The average sleep duration for an adult is approximately 7 h per day. Sleep is necessary to regenerate physical and psychological state of a human. A bad sleep quality has a major impact on the health status and can lead to different diseases. In this paper an approach will be presented, which uses a long-term monitoring of vital data gathered by a body sensor during the day and the night supported by mobile application connected to an analyzing system, to estimate sleep quality of its user as well as give recommendations to improve it in real-time. Actimetry and historical data will be used to improve the individual recommendations, based on common techniques used in the area of machine learning and big data analysis.