Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary

Kernel adaptive filters, a class of adaptive nonlinear time-series models, are known by their ability to learn expressive autoregressive patterns from sequential data. However, for trivial monotonic signals, they struggle to perform accurate predictions and at the same time keep computational complexity within desired boundaries. This is because new observations are incorporated to the dictionary when they are far from what the algorithm has seen in the past. We propose a novel approach to kernel adaptive filtering that compares new observations against dictionary samples in terms of their unit-norm (normalised) versions, meaning that new observations that look like previous samples but have a different magnitude are not added to the dictionary. We achieve this by proposing the unit-norm Gaussian kernel and define a sparsification criterion for this novel kernel. This new methodology is validated on two real-world datasets against standard KAF in terms of the normalised mean square error and the dictionary size.Comment: Accepted at the IEEE Digital Signal Processing conference 201

Reconfigurable Microwave Photonic Topological Insulator

Using full 3D finite element simulation and underlining Hamiltonian models, we demonstrate reconfigurable photonic analogues of topological insulators on a regular lattice of tunable posts in a re-entrant 3D lumped element type system. The tunability allows dynamical {\it in-situ} change of media chirality and other properties via alteration of the same parameter for all posts, and as a result, great flexibility in choice of bulk/edge configurations. Additionally, one way photon transport without an external magnetic field is demonstrated. The ideas are illustrated by using both full finite element simulation as well as simplified harmonic oscillator models. Dynamical reconfigurability of the proposed systems paves the way to a new class of systems that can be employed for random access, topological signal processing and sensing

Global Representation of the Fine Structure Constant and its Variation

The fine structure constant, alpha, is shown to be proportional to the ratio of the quanta of electric and magnetic flux of force of the electron, and provides a new representation, which is global across all unit systems. Consequently, a variation in alpha was shown to manifest due to a differential change in the fraction of the quanta of electric and magnetic flux of force, while a variation in hcross.c was shown to manifest due to the common mode change. The representation is discussed with respect to the running of the fine structure constant at high energies (small distances), and a putative temporal drift. It is shown that the running of the fine structure constant is due to equal components of electric screening (polarization of vacuum) and magnetic anti-screening (magnetization of vacuum), which cause the perceived quanta of electric charge to increase at small distances, while the magnetic flux quanta decreases. This introduces the concept of the bare magnetic flux quanta as well as the bare electric charge. With regards to temporal drift, it is confirmed that it is impossible to determine which fundamental constant is varying if alpha varies.Comment: Final accepted version for Metrologia. This version includes a proof that the representation of the fine structure constant is global across all unit systems, using Jackson's global representation of Maxwell's equations (which is also valid for all unit systems). The version is shorter than the previous, thus the discussion throughout is more brie