Asynchronous Pulse Code Multiple Access (APCMA) has been proposed as a pulse-based LPWA technology for massive IoT, offering robustness against collisions. However, in ultra-dense environments, pulse overlaps cause misdetections. This paper proposes an enhanced CSS-APCMA decoding method that leverages per-message chirp frequency-offset consistency and accounts for clock jitter. The receiver was implemented using a USRP and GNU Radio, and large-scale experiments were conducted with 1,000 transmitters operating in the 920 MHz band. The results demonstrate that the proposed method maintains a high reception rate while significantly suppressing misdetections and achieving higher data rates compared to conventional CSS-APCMA in ultra-dense environments.

This paper shares the challenges facing today's augmented reality (AR) smartphone applications, particularly in the realm of localization and tracking failure. Our research identifies limitations in current vision-based landmarks such as QR codes and AprilTags, commonly used to aid in localization, and the drawbacks of LiDAR integration in variable lighting conditions, compromising AR's accuracy and functionality. We also examine the constraints of Inertial Measurement Units (IMU) on movement speed, highlighting its impact on the dynamic performance of AR applications. Based on our extensive 316 experimental cases for 113 hours, including 34 case studies with 17 subjects in 2 sites, this paper presents the field with a nuanced analysis of the failure modes inherent in smartphone-based AR localization. We further explore a prototype solution which fuses ultra-wideband (UWB)-based sensing with the vision-based systems to alleviate these failure modes. Our approach addresses the immediate challenges of AR localization and opens avenues for future research and development in creating more spatially aware and interactive digital worlds. All of our demonstration videos, code, and datasets are available here1.

Collaborative learning has been qualitatively analyzed by learning science researchers to enhance learning performance. A quantitative analysis system supports the existing qualitative analysis of collaborative learning. We propose an Internet of Things (IoT) system comprising business-card-type badges, radio-over-fiber (RoF)-based synchronization, and a collaboration analysis algorithm to support collaborative learning analytics in different venues (e.g., offline, online, and hybrid). We showed that our proposed system quantitatively supports qualitative analysis of collaborative learning in different environments.

This paper proposes an iterative low complexity soft input decoding with assistance of the lattice reduction for overloaded MIMO. The proposed soft input decoding applies two types of lattice reduction-aided linear filters to estimate log-likelihood ratio (LLR) in order to reduce the computational complexity. A lattice reduction-aided linear with whitening filter is introduced for the LLR estimation in the proposed decoding. The equivalent noise caused by the linear filter is mitigated with the decoder output streams and the LLR is re-estimated after the equivalent noise mitigation. Furthermore, LLR clipping is introduced in the proposed decoding to avoid the performance degradation due to the incorrect LLRs. The proposed decoding achieves about 2dB better BER performance than the soft decoding with the exhaustive search algorithm, so called the MLD, at the BER of 1.0E-4, even though the complexity of the proposed decoding is 1/10 as small as that of the soft decoding with the exhaustive search.