Currently, many post-quantum cryptography schemes have been implemented on various hardware platforms in order to provide efficient solutions in cybersecurity services. As researchers and hardware developers focus primarily on designs providing small latency and requiring fewer hardware resources, their implementations could seldom omit protection techniques against various physical attacks. This paper studies potential attacks on the cryptography implementations that run on Field-Programmable Gate Array (FPGA) platforms. We mainly analyze how Post-Quantum Cryptography (PQC) implementations could be vulnerable on various platforms. Further, we aim at the FPGA-based implementations of National Institute of Standards and Technology (NIST)’s PQC competition finalists. Our study should present to developers the current overview of attacks and countermeasures that can be implemented on specific PQC schemes on FPGA platforms. Moreover, we present novel implementation of one universal countermeasure component and reveal additional resources that are needed.

We observe that in many applications, the motion present in a scene is well characterized by a small number of (rigid) motion hypotheses. Based on this observation, we present rigid multi-motion optical flow (RMM). By restricting flow to one of several motion hypotheses, RMM produces more accurate optical flow than arbitrary motion models. We evaluate an algorithm based on RMM on a novel synthetic dataset, consisting of 12 photo-realistically rendered scenes containing rigid vehicular motion and a corresponding, exact, ground truth. On this dataset, we demonstrate a substantial advantage of RMM over general-purpose algorithms: going from 36% outliers with the DiscreteFlow algorithm, to 26% with ours, with a mean error reduction from 8.4px to 6.9px. We also perform qualitative evaluation on real-world imagery from traffic cameras.

In order to facilitate the introduction of transit tracking and arrival time prediction in smaller transit agencies, we investigate an automatic, smartphone-based system which we call EasyTracker. To use EasyTracker, a transit agency must obtain smartphones, install an app, and place a phone in each transit vehicle. Our goal is to require no other input.

EasyTracker is an automated system that assists small public or private transit agencies in deploying bus tracking and arrival time prediction. This demo will showcase how data from GPS sensors embedded in smartphones can be automatically processed in order to accurately estimate routes, bus stop locations, schedules, and make annotated maps with real-time bus tracking and arrival time predictions. We will also demonstrate a website portal which transit agencies can use to further interact with their bus transit systems.

Real-time transit tracking is gaining popularity as a means for transit agencies to improve the rider experience. However, many transit agencies lack either the funding or initiative to provide such tracking services. In this paper, we describe a crowd-sourced alternative to official transit tracking, which we call cooperative transit tracking.

A transit navigation system is described that integrates real-time transit and user tracking with existing transit schedules to improve the transit riding experience.