International audienceWe develop a new algorithm for geospatial tagging for Internet-of-Things (IoT) type applications, which we denote as location-of-things (LoT). The underlying idea of LoT applications is to use low-cost off-the-shelf two-way time-of-arrival (TW-ToA) ranging devices to perform localization of tags. We first demonstrate how conventional TW-ToA localization algorithms may experience performance degradation in cases where some of the access points (APs) are outside the communication range of the tags. We then show that we can make use of the audibility information (which indicates whether an AP is able or unable to communicate with the tags). By leveraging on this available information, we re-formulate the localization problem as a statistical nonlinear estimation problem. This information, coupled with ranging observations from audible AP leads to a new maximum likelihood estimation (MLE) algorithm for the tag's location. Our approach provides considerable improvement of the localization performance by mitigating the well-known ambiguity problem which arises when only a few AP are audible. In addition, we derive the Cramér-Rao bound (CRB) of the source location estimate under the proposed framework

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In the absence of a single module interface standard, integration of pre-designed modules in System-on-Chip design often requires the use of protocol converters. Existing approaches to automatic synthesis of protocol converters mostly lack formal foundations and either employ abstractions that ignore crucial low level behaviors, or grossly simplify the structure of the protocols considered. We present a state-machine based formal model for bus based communication protocols, and precisely define protocol compatibility, and correct protocol conversion. Our model is expressive enough to capture features of commercial protocols such as bursts, pipelined transfers, wait state insertion, and data persistence, in cycle accurate detail. We show that the most general, correct converter for a pair of protocols, can be described as the greatest fixed point of a function for updating buffer states. This characterization yields a natural algorithm for automatic synthesis of a provably correct converter by iterative computation of the fixed point. We report our experience with automatic converter synthesis between widely used commercial bus protocols, such as AMBA AHB, ASB, APB, and OCP, considering features which are beyond the scope of current techniques.

COVID-19 has accelerated the adoption of online learning, and the authors' university (like many others) is settling into a blended learning approach. In this chapter, the authors share their experiences in improving students' self-regulation of online learning. The first learning experience for most students at the Singapore Institute of Technology is in online, self-paced courses in Mathematics, Physics, and Chemistry. Beyond content, these courses provide students opportunities to embrace the ‘norm' of self-paced, self-directed online learning and to expose and encourage them to develop self-regulation skills. Students are required to self-assess and refer to different learning resources independently, based on their identified needs. The authors have also developed another intervention based on Zimmerman's self-regulated learning model, guiding students to plan, monitor, and adjust their learning plans and develop self-efficacy through this process. For students to transfer these skills into their actual studies, the authors provide individual coaching sessions to facilitate students' implementation.