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Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
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The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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Towards a new PhD Curriculum for Digital Finance.

Lennart John Baals1, Jörg R Osterrieder1,2, Branka Hadji-Misheva1

  • 1Institute of Applied Data Science and Finance, Bern University of Applied Science, Bern, Bern, 3005, Switzerland.

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Summary
This summary is machine-generated.

A new doctoral curriculum in Digital Finance is proposed to train researchers for modern finance challenges. This interdisciplinary program focuses on research, advanced training, transferable skills, and practical experience to boost innovation.

Keywords:
Digital Finance; Artificial Intelligence (AI); Blockchain Technology; Sustainable Finance; Financial Education and Training

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Area of Science:

  • Digital Finance
  • Interdisciplinary Doctoral Training
  • Financial Innovation

Background:

  • The digital finance field is rapidly evolving, requiring specialized interdisciplinary doctoral training.
  • Existing training programs may not adequately address the complex skill set needed for modern finance.

Purpose of the Study:

  • To present a comprehensive doctoral curriculum for digital finance.
  • To equip early-stage researchers with essential skills for navigating contemporary financial complexities.

Main Methods:

  • Curriculum structured around four pillars: Research and Mandatory Scientific Training, Advanced Scientific Training, Transferable Skills Training, and Training through Secondments.
  • Emphasis on international collaboration via conferences and accessible online courses.
  • Integration with leading institutions and alignment with the European Digital Finance Package.

Main Results:

  • The proposed curriculum offers a balanced mix of theoretical knowledge, practical experience, and soft skills.
  • The program addresses critical challenges including data quality, AI model deployment, trust in AI products, and labor shortages.
  • Fosters innovation and competitiveness within the European Finance industry.

Conclusions:

  • The comprehensive curriculum is designed to meet the evolving demands of the digital finance sector.
  • It aims to produce highly skilled researchers capable of driving innovation and addressing industry challenges.
  • The program's structure and focus ensure relevance and potential for significant impact in European finance.