As a new paradigm for signal processing, compressed sensing (CS), allows the acquisition of signals at rates that are far below their Nyquist rate comparable to the traditional laws of sampling and reconstruction. CS offers a complete and mathematically rigorous framework for preserving the quality of reconstruction while reducing sampling requirements in high-dimensional systems where signals are intrinsically sparse or compressible in some transform domain. In this research paper, we study the design and development of AFE architectures driven by compressed sensing for sub-Nyquist signal acquisition through complex high-dimensional setups.

Power electronic converters are fundamental components in modern electrical and energy systems, supporting applications such as renewable energy integration, electric mobility, and intelligent power distribution. Despite their widespread use, the dynamic behavior of these converters under extreme operating conditions remains insufficiently understood, particularly when nonlinear effects dominate system performance. Traditional linearized models often fail to capture the complex interactions introduced by high switching frequencies, abrupt load transients, thermal stress, and input voltage disturbances.

Recently, there has been exciting advance reported in manipulation of free-space optical (classical and quantum) beams by well-designed plasmatic Nano-structures. These engines or pre-processors also utilize meta metallic nanostructures to generate non-local fields for ultra-fast response electro-optic manipulation of optical elements that exhibit bitwise signal changes and decoding transfers via signals within the material structures.

The relentless trend of semiconductor technologies scaling into the deep nanometer regime has significantly changed the performance, reliability and design methodologies for VLSI systems. Traditionally, reduced transistor sizes have been the driving force behind performance improvements; however, as modern integrated circuits approach the limits of scaling, interconnects are becoming one of the main bottlenecks.

The fast miniaturization trend of the electronic components has pushed semiconductor industry to the Nano scale regime where classical transport theories usually not able to provide accurate descriptions on charge carrier behaviors. At the atomic scale, as device dimensions continue to shrink toward molecular sizes, quantum mechanical effects — tunnelling, wave interference, ballistic transport and electron confinement and quantum coherence — begin to dominate the electrical properties of materials and devices [22]. As a result, quantum transport modeling has become one of the key research topics towards understanding and controlling next-generation Nano electronic devices.

The exponential increase of generative artificial intelligence (AI) has introduced a new paradigm in the way we conceive, model and validate engineering systems. AI technologies are commonly added as helper appendices to good old computer-aided design (CAD) and system engineering workflows, which remain largely deterministic automated and manual despite strong organisation and rich in tools.

On the one side, engineering design is taking on a new dimension as artificial intelligence (AI) and self-governing software agents are beginning to augment and automate intricate creative, analytical and optimisation routines. Traditional infrastructure engineering and CAD methods are still predominantly manual iterative simulation, integrating experts’ reasoning, heuristic improvement.