FPGA implementation of LDPC soft-decision decoders based DCSK for spread spectrum applications

<span lang="EN-US">Spread spectrum (SS) communications have attracted interest because of their channel attenuation immunity and low intercept potential. Apart from some extra features such as basic transceiver structures, chaotic communication would be the analog alternative to digital SS systems. Differential chaos shift keying (DCSK) systems, non-periodic and random characteristics among chaos carriers as well as their interaction with soft data are designed based on low-density parity-check (LDPC) codes in this brief. Because of simple structure, and glorious ability to <span>correct errors. Using the Xilinx kintex7 FPGA development kit, we investigate the hardware performance and resource requirement tendencies of the DCSK</span> communication system based on LDPC decoding algorithms (Prob. Domain, Log Domain and Min-Sum) over AWGN channel. The results indicate that the proposed system model has substantial improvements in the performance of the bit error rate (BER) and the real-time process. The Min-Sum decoder has relatively fewer FPGA resources than the other decoders. The implemented system will achieve 10-4 BER efficiency with 5 dB associate E_b/N_o as a coding gain.</span>

Challenges and Resilience-Building: A Narrative Inquiry Study on a Mid-Career Chinese EFL Teacher

With a high rate of attrition and burnout of teachers as a global concern, teacher resilience has become a trendy topic in the research of their professional development as one of the pillars of positive psychology (positive character traits). However, the literature reveals that little research has been done on the mid-career teachers in the Chinese context, especially on how resilience may be nurtured, sustained, or eroded over time. Focusing on a mid-career EFL female teacher (the author) in China as a case study, this longitudinal self-reflective study employs a narrative inquiry to investigate the challenges that the experienced teacher was encountered with and to depict her trajectories of resilience-building by fleshing out the interaction between challenges, resources, and coping strategies in her three different scenarios. “Hard data,” such as teaching journals, reflective field notes, and messages with students were collected and analyzed inductively by using thematic analysis, and “soft data,” like memory was also referred to. The findings unfolded challenges confronting the experienced teacher peculiar to the Chinese context and charted a detailed bumpy journey of resilience building in three phases, accompanied by her growing emotional, intellectual, and psychological capacities. Implications are drawn out for teacher resilience building, school leaders, and policymakers.

Greatly Exaggerated in Canada: Diverging Data and Media Bailouts

Background: The Canadian government allocated $595 million in subsidies over five years to news media in 2019, but the bailout was based on questionable data. Financial losses were exaggerated; a think tank report was criticized for using data selectively; data from a university research project differed sharply from annual industry counts; and job loss figures were disputed. Analysis: Hard data can diverge markedly from soft data accepted in pursuit of policy outcomes. Conclusions and implications: A second campaign underway on behalf of entertainment industries could yield a bailout several times larger than the first. Closer scrutiny should be exercised of media narratives and offered data. An independent media research centre should collect and verify data for policy purposes.Contexte : En 2019, le gouvernement canadien a octroyé aux médias d’information 595 millions de dollars en subventions étalées sur cinq ans, un montant évalué à partir de données douteuses. En effet, on a surestimé les pertes financières dans le milieu; le rapport influent d’un groupe de réflexion se fondait sur des données sélectionnées pour les besoins de la cause; les données provenant d’un projet de recherche universitaire différaient beaucoup de celles fournies annuellement par l’industrie; et on a exagéré les pertes d’emploi. Analyse : Les données dures peuvent différer énormément des données molles acceptées dans le but d’atteindre certains objectifs politiques. Conclusion et implications : Une seconde campagne menée pour aider les industries du divertissement pourrait bénéficier de subventions encore plus généreuses que les premières. Avant de procéder, il serait judicieux d’examiner de près les narratifs des médias et les données proposées. À cet égard, on devrait créer un centre indépendant pour la recherche sur les médias qui pourrait lui même recueillir et vérifier les données utilisées pour formuler des politiques.

A Novel MADM Framework under q-Rung Orthopair Fuzzy Bipolar Soft Sets

In many real-life problems, decision-making is reckoned as a powerful tool to manipulate the data involving imprecise and vague information. To fix the mathematical problems containing more generalized datasets, an emerging model called q-rung orthopair fuzzy soft sets offers a comprehensive framework for a number of multi-attribute decision-making (MADM) situations but this model is not capable to deal effectively with situations having bipolar soft data. In this research study, a novel hybrid model under the name of q-rung orthopair fuzzy bipolar soft set (q-ROFBSS, henceforth), an efficient bipolar soft generalization of q-rung orthopair fuzzy set model, is introduced and illustrated by an example. The proposed model is successfully tested for several significant operations like subset, complement, extended union and intersection, restricted union and intersection, the ‘AND’ operation and the ‘OR’ operation. The De Morgan’s laws are also verified for q-ROFBSSs regarding above-mentioned operations. Ultimately, two applications are investigated by using the proposed framework. In first real-life application, the selection of land for cropping the carrots and the lettuces is studied, while in second practical application, the selection of an eligible student for a scholarship is discussed. At last, a comparison of the initiated model with certain existing models, including Pythagorean and Fermatean fuzzy bipolar soft set models is provided.

Roadmapping in the Era of Uncertainty: How to Integrate Data-Driven Methods with Expert Insights

Roadmapping has long been regarded as a practical tool for supporting decision-making for science and technology innovation and it has received recent attention for its potential use in responses to uncertainty. Indeed, roadmapping enables forward-looking strategy making and thus helps to reduce uncertainty. Accordingly, numerous studies have been conducted to propose new approaches to roadmapping for a wide range of contexts, including the data-driven and expert-based approaches. Although these two main approaches have distinct advantages and disadvantages, few previous studies have focused on how to integrate them into roadmapping to better support decision-making related to science and technology innovation. To address this research gap, this study investigated how to integrate data-driven approaches with expert insights during roadmapping. For this purpose, a workshop-based roadmapping method was combined with data-driven methods to test this approach in the context of technology planning for the automobile industry. An ethnographic approach was used to collect data on when, where, and how data analysis must be conducted to support experts’ discussions. The research findings open a discussion regarding how to integrate data-driven methods with expert insights during roadmapping based on the trade-offs between the two types of data, that is, hard data for data-driven methods and soft data from expert insights, and suggest possible opportunities for future roadmapping developments.

Visualizing the potential for transit-oriented development: Insights from an open and interactive planning support tool in Flanders, Belgium

This paper reports on the development trajectory of an empirical tool for transit-oriented development planning in Flanders, Belgium. The tool, StationsRadar, draws on a branch of empirical railway station assessment tools that aim to support transit-oriented development planning processes by visualizing the performance of station locations for a range of transport (‘node’) and land use (‘place’) accessibility indicators. At the root of this paper lies the observation that, while the vast majority of reviewed studies highlight the relevance of the developed tools for planning practice, little work is undertaken to systematically verify that claim. Against this backdrop, we invoke an experiential research strategy as recently proposed in the field of planning research; we organize a series of experiential workshops in which we probe the tool’s added value for regional planning in Flanders. In the process, we specifically work towards a qualitative appraisal of tool ‘usability’ and discuss how our findings bear relevance to the well-rehearsed practice of developing empirical transit-oriented development support tools. Additionally, we elaborate on and illustrate the ramifications of our findings in terms of the subsequent/iterative technical revision of the tool. We conclude this paper by putting forward three major usability recommendations pertaining to: interactive and diversified data visualizations, actor-mobilizing momentum in light of data transparency, and the integration of ‘hard’ and ‘soft’ data in light of crowdsourcing aspirations. We reflect on the broader technical and methodological challenges that come with implementing these in practice.