FROM SIGNAL THEORY TO DEPLOYABLE REAL-TIME EMBEDDED DSP: A COMPARATIVE QUALITATIVE STUDY OF PERCEIVED GAPS IN MATHEMATICAL READINESS FOR INDUSTRY ROLES IN PAKISTAN AND THE UNITED KINGDOM

Abstract

This study explored perceived gaps in mathematical readiness for real-time embedded Digital Signal Processing (DSP) roles among university students in Pakistan and the United Kingdom (UK). A qualitative comparative design was employed, and semi-structured interviews were conducted with 20 students, including 10 participants from each country. Reflexive thematic analysis identified five interrelated themes: gaps in fundamental mathematical knowledge, difficulty transferring theory to practical DSP applications, differences in educational and technological support, limited integration of artificial intelligence (AI), and misalignment between educational preparation and perceived industry expectations. Participants in both contexts reported familiarity with key mathematical principles but experienced difficulty translating concepts such as Fourier transforms, calculus, and linear algebra into algorithms, simulations, and real-time embedded implementations. Pakistani participants generally reported greater limitations in access to specialized laboratories, industry-standard software, embedded platforms, and industry-informed academic support, whereas UK participants described comparatively stronger resources but continued to experience theory–practice difficulties. AI was perceived as potentially useful for visualization, simulation, coding assistance, and feedback; however, its curricular integration remained limited and required greater emphasis on verification and mathematical reasoning. The findings highlighted the need to align mathematical instruction with simulation, programming, hardware implementation, authentic assessment, and industry-linked learning. The study provided context-sensitive recommendations for strengthening students’ preparation for real-time embedded DSP roles.