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Mary Samuel

Publications and source records attributed to Mary Samuel.

6 recordsLinked to original sources
arXiv2023

Improved Vehicle Sub-type Classification for Acoustic Traffic Monitoring

The detection and classification of vehicles on the road is a crucial task for traffic monitoring. Usually, Computer Vision (CV) algorithms dominate the task of vehicle classification on the road, but CV methodologies might suffer in poor lighting conditions and require greater amounts of computational power. Additionally, there is a privacy concern with installing cameras in sensitive and secure areas. In contrast, acoustic traffic monitoring is cost-effective, and can provide greater accuracy, particularly in low lighting conditions and in places where cameras cannot be installed. In this paper, we consider the task of acoustic vehicle sub-type classification, where we classify acoustic signals into 4 classes: car, truck, bike, and no vehicle. We experimented with Mel spectrograms, MFCC and GFCC as features and performed data pre-processing to train a simple, well optimized CNN that performs well at the task. When used with MFCC as features and careful data pre-processing, our proposed methodology improves upon the established state-of-the-art baseline on the IDMT Traffic dataset with an accuracy of 98.95%.

cs.SD↗
arXiv2022

Visual Transformer for Soil Classification

Our food security is built on the foundation of soil. Farmers would be unable to feed us with fiber, food, and fuel if the soils were not healthy. Accurately predicting the type of soil helps in planning the usage of the soil and thus increasing productivity. This research employs state-of-the-art Visual Transformers and also compares performance with different models such as SVM, Alexnet, Resnet, and CNN. Furthermore, this study also focuses on differentiating different Visual Transformers architectures. For the classification of soil type, the dataset consists of 4 different types of soil samples such as alluvial, red, black, and clay. The Visual Transformer model outperforms other models in terms of both test and train accuracies by attaining 98.13% on training and 93.62% while testing. The performance of the Visual Transformer exceeds the performance of other models by at least 2%. Hence, the novel Visual Transformers can be used for Computer Vision tasks including Soil Classification.

cs.CV↗
arXiv2017

On dendrites, generated by polyhedral systems and their ramification points

The paper considers systems of contraction similarities in $\mathbb R^d$ sending a given polyhedron $P$ to polyhedra $P_i\subset P$, whose non-empty intersections are singletons and contain the common vertices of those polyhedra, while the intersection hypergraph of the system is acyclic. It is proved that the attractor $K$ of such system is a dendrite in $\mathbb R^d$. The ramification points of such dendrite fave finite order whose upper bound depends only on the polyhedron $P$, and the set of the cut points of the dendrite $K$ is equal to the dimension of the whole $K$ iff $K$ is a Jordan arc.

math.MG↗