arXiv subjects

Ganesh Babu

Publications and source records attributed to Ganesh Babu.

3 recordsLinked to original sources

arXiv2026

Carbon Farming: An Expository, Inter-Disciplinary Survey

Carbon farming is the collection of agricultural best practices specifically designed to maximize the capture and long-term storage of atmospheric carbon dioxide in soils and plant biomass, while simultaneously reducing greenhouse gas emissions from cultivation practices. Carbon farming can be viewed as a promising pathway to simultaneously address climate change mitigation, soil degradation, and farmer welfare. For example, if the entire agricultural cropland in India practices carbon farming, this will spectacularly offset about 50% of emissions from the country's annual transport-sector emissions. However, practical deployment of carbon farming is constrained by scientific challenges, inherent complexity, and fragmented understanding across disciplines. This inter-disciplinary, expository survey offers the first unified treatment of carbon farming for practitioners, policymakers, and researchers. The survey integrates insights from agronomy, soil science, climate science, measurement, reporting, and verification (MRV), economics, carbon markets, and policy design. We begin by establishing the conceptual foundations of soil organic carbon dynamics and agricultural carbon sequestration, and compare carbon farming with the paradigms of sustainable, regenerative, and organic agriculture. We then present a comprehensive landscape analysis of carbon-farming best practices, including both generic and crop-specific interventions, and systematically examine their co-benefits and trade-offs. The paper offers a rigorous review of MRV frameworks, emerging digital MRV technologies, and the carbon-credit project life cycle, followed by a structured analysis of voluntary and compliance carbon markets...

econ.GN↗

arXiv2024

A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images

The use of hyperspectral imaging to investigate food samples has grown due to the improved performance and lower cost of instrumentation. Food engineers use hyperspectral images to classify the type and quality of a food sample, typically using classification methods. In order to train these methods, every pixel in each training image needs to be labelled. Typically, computationally cheap threshold-based approaches are used to label the pixels, and classification methods are trained based on those labels. However, threshold-based approaches are subjective and cannot be generalized across hyperspectral images taken in different conditions and of different foods. Here a consensus-constrained parsimonious Gaussian mixture model (ccPGMM) is proposed to label pixels in hyperspectral images using a model-based clustering approach. The ccPGMM utilizes information that is available on some pixels and specifies constraints on those pixels belonging to the same or different clusters while clustering the rest of the pixels in the image. A latent variable model is used to represent the high-dimensional data in terms of a small number of underlying latent factors. To ensure computational feasibility, a consensus clustering approach is employed, where the data are divided into multiple randomly selected subsets of variables and constrained clustering is applied to each data subset; the clustering results are then consolidated across all data subsets to provide a consensus clustering solution. The ccPGMM approach is applied to simulated datasets and real hyperspectral images of three types of puffed cereal, corn, rice, and wheat. Improved clustering performance and computational efficiency are demonstrated when compared to other current state-of-the-art approaches.

stat.ME↗

arXiv2020

Verification of a Generative Separation Kernel

We present a formal verification of the functional correctness of the Muen Separation Kernel. Muen is representative of the class of modern separation kernels that leverage hardware virtualization support, and are generative in nature in that they generate a specialized kernel for each system configuration. These features pose substantial challenges to existing verification techniques. We propose a verification framework called conditional parametric refinement which allows us to formally reason about generative systems. We use this framework to carry out a conditional refinement-based proof of correctness of the Muen kernel generator. Our analysis of several system configurations shows that our technique is effective in producing mechanized proofs of correctness, and also in identifying issues that may compromise the separation property.

cs.PL↗