44 research outputs found
Quest of Data Colonialism and Cyber Sovereignty: India’s Strategic Position in Cyberspace
No full textThe dawn of the neocolonial project has seen the emergence of a new space: data. Data is a raw material that can be stitched, processed and marketed in the same way as the East India Company (EIC) used to do with India’s cotton. EIC, which started as one of the world’s first joint-stock companies, turned into a wild beast, building a corporate lobby with the help of lawyers and MP shareholders to amend legislation in its favor. The EIC became a particularly atrocious and innovative colonial project that directly or indirectly controlled continents, thanks to an army larger than the army of any nation-state at the time. The Drain Theory of Dadabhai Naroji have opened India’s eyes to how the EIC was taking raw material from the country and converting it into a finished product that was marketed in India again in the same way as raw data is being processed outside India and then marketed here today. In today’s digital era, big corporations need not own big armies, as companies are protected by nation-states and bailed out when required. Today, one does not need to travel overseas to explore and conquer Gold, God and Glory; instead, they are a click away. The neocolonial project runs on digital platforms, while the popular narrative of bridging the digital divide and giving internet access to millions of people resembles the idea of the “white savior” liberating the “noble savage” through modern Western education. Facebook’s grand plan of providing free internet to all can be best understood as a neocolonial strategy to mine the data of billions by equating it with water and land. Similarly, the Cambridge Analytica scandal provides an example of how neocolonial forces can influence the fundamental democratic process of electing a government. Therefore, nations endorsing democratic values should be especially wary of the trap of neocolonialist forces, as such nations are particularly vulnerable to their project. This paper critically study the cyber security infrastructure and policies in India and analyze the India’s approach towards cyber sovereignty and data colonialism and thereafter examine the India’s strategic position in cyberspace and suggest policy recommendations
AI Contributions and Authorship
No full textThis component documents how AI tools were used to assist with grammar, formatting, and clarity of the AeroLink MedBridge documentation. The conceptual innovation and intent originate fully from the author. This section ensures transparency about the role of AI in developing the publication
Reliable urban vehicle localization under faulty satellite navigation signals
No full textAbstract Reliable urban navigation using global navigation satellite system requires accurately estimating vehicle position despite measurement faults and monitoring the trustworthiness (or integrity) of the estimated location. However, reflected signals in urban areas introduce biases (or faults) in multiple measurements, while blocked signals reduce the number of available measurements, hindering robust localization and integrity monitoring. This paper presents a novel particle filter framework to address these challenges. First, a Bayesian fault-robust optimization task, formulated through a Gaussian mixture model (GMM) measurement likelihood, is integrated into the particle filter to mitigate faults in multiple measurement for enhanced positioning accuracy. Building on this, a novel test statistic leveraging the particle filter distribution and the GMM likelihood is devised to monitor the integrity of the localization by detecting errors exceeding a safe threshold. The performance of the proposed framework is demonstrated on real-world and simulated urban driving data. Our localization algorithm consistently achieves smaller positioning errors compared to existing filters under multiple faults. Furthermore, the proposed integrity monitor exhibits fewer missed and false alarms in detecting unsafe large localization errors
Neural Elevation Models for Terrain Mapping and Path Planning
No full textThis work introduces Neural Elevations Models (NEMos), which adapt Neural
Radiance Fields to a 2.5D continuous and differentiable terrain model. In
contrast to traditional terrain representations such as digital elevation
models, NEMos can be readily generated from imagery, a low-cost data source,
and provide a lightweight representation of terrain through an implicit
continuous and differentiable height field. We propose a novel method for
jointly training a height field and radiance field within a NeRF framework,
leveraging quantile regression. Additionally, we introduce a path planning
algorithm that performs gradient-based optimization of a continuous cost
function for minimizing distance, slope changes, and control effort, enabled by
differentiability of the height field. We perform experiments on simulated and
real-world terrain imagery, demonstrating NEMos ability to generate
high-quality reconstructions and produce smoother paths compared to discrete
path planning methods. Future work will explore the incorporation of features
and semantics into the height field, creating a generalized terrain model
