Next-generation communication systems operating in the mm-Wave and sub-THz bands face high path loss, which can be mitigated by ultra-massive antenna arrays. However, high-resolution quantization in these systems is often impractical, leading to a growing interest in low-resolution, particularly 1-bit ADCs. This letter addresses joint angle and range estimation using 1-bit massive uniform linear arrays (ULA) in narrowband near-field systems. We propose a MUSIC-based 1-bit joint angle-range estimation algorithm (JARE-MUSIC) over a near-field narrowband channel, thus, demonstrating that existing MUSIC-based methods can be adapted for effective near-field localization even under extreme quantization.

This paper presents a wireless power transfer (WPT) for a mid-sized inspection mobile robot. The objective is to transmit 100 W of power over 1 meter of distance, achieved through lightweight Litz wire coils weighing 320 g held together with a coil structure of 3.54 kg. The Wireless Power Transfer System (WPTS) is mounted onto an unmanned ground vehicle (UGV). The study addresses an investigation of coil design, accounting for misalignment and tolerance issues in resonancecoupled coils. In experimental validation, the system effectively transmits 109.7 W of power over a 1-meter distance, with obstacles present. This achievement yields a system efficiency of 47.14%, a value that is remarkably close to the maximum power transfer point (50%) when the WPTS utilises the full voltage allowance of the capacitor. The paper shows the WPTS charging speed of 5 minutes for 12 V, 0.8 Ah lead acid batteries.

This research delves into the complex relationship between Urban Green Spaces (UGS) and crime rates in four distinct U.S. cities: Orlando, Baltimore, Cincinnati, and Sacramento. Debates surrounding this correlation range from the belief that increased vegetation leads to heightened crime to the contrary perspective. Our study challenges the common assumption that more greenery equates to reduced crime, revealing nuanced and diverse patterns in the examined cities. Employing a comprehensive approach, we integrate crime data from the Federal Bureau of Investigation with satellite imagery classification using a Convolutional Neural Network (CNN). The methodology encompasses pre-processing Landsat imagery, generating spectral indices, and training the CNN for green coverage pixel classification. Acknowledging limitations such as image classification challenges, absence of multivariate analysis, spatial resolution constraints, and crime reporting biases, we underscore the necessity for future research refinements. This study prompts a reassessment of assumptions and underscores the significance of considering multiple variables to attain a thorough understanding of the intricate interplay between urban greenery and crime rates.