Land Information New Zealand (LINZ; Māori: Toitū Te Whenua) is the public service department of New Zealand charged with geographical information and surveying functions as well as handling land titles, and managing Crown land and property. The minister responsible is the Minister for Land Information, and was formerly the Minister of Survey and Land Information. The New Zealand Geographic Board secretariat is part of LINZ and provides the Board with administrative and research assistance and advice.The Minister for Land Information is Eugenie Sage.Gaye Searancke was appointed Chief Executive of Land Information New Zealand in August 2019. She succeeded Andrew Crisp, who had been in the post since 2016.
Atmospheric tomography, the problem of reconstructing the atmospheric turbulence volume from wavefront sensor measurements, is an integral part of many adaptive optics systems. It is used to enhance the image quality of ground-based telescopes, such as for the Multiconjugate Adaptive Optics Relay For ELT Observations (MORFEO) instrument on the Extremely Large Telescope (ELT). To solve this problem, a singular value-type decomposition (SVTD)-based approach has been proposed in previous research. In this paper, we focus on the numerical implementation of this SVTD approach, leading to the singular value decomposition-based Atmospheric Tomography with Fourier Domain Regularization Algorithm (SAFR), and investigate its performance for Multi-Conjugate Adaptive Optics (MCAO) systems. The key features of the SAFR algorithm are the utilization of the FFT and the precomputation of computationally demanding parts. Together, this yields a fast algorithm with less memory requirements than commonly used matrix vector multiplication (MVM) approaches. We evaluate the performance of SAFR regarding reconstruction quality and computational expense in numerical experiments using the simulation environment COMPASS, in which we use an MCAO setup resembling the physical parameters of the MORFEO instrument of the ELT.
Spiking Neural Networks (SNNs) offer a promising solution to the problem of increasing computational and energy requirements for modern Machine Learning (ML) applications. Due to their unique data representation choice of using spikes and spike trains, they mostly rely on additions and thresholding operations to achieve results approaching state-of-the-art (SOTA) Artificial Neural Networks (ANNs). This advantage is hindered by the fact that their temporal characteristic does not map well to already existing accelerator hardware like GPUs. Therefore, this work will introduce a hardware accelerator architecture capable of computing feedforward LIF-only SNNs, as well as an accompanying encoding method to efficiently encode already existing data into spike trains. Together, this leads to a design capable of >99 MNIST dataset, with 0.29ms inference times on a Xilinx Ultrascale+ FPGA, as well as 0.17ms on a custom ASIC using the open-source predictive 7nm ASAP7 PDK. Furthermore, this work will showcase the advantages of the previously presented differential time encoding for spikes, as well as provide proof that merging spikes from different synapses given in differential time encoding can be done efficiently in hardware.
