Indian Institute of Technology Guwahati

About: Indian Institute of Technology Guwahati is a education organization based out in Guwahati, Assam, India. It is known for research contribution in the topics: Adsorption & Catalysis. The organization has 6933 authors who have published 17102 publications receiving 257351 citations.

Drowsy driver detection using representation learning

Abstract: The advancement of computing technology over the years has provided assistance to drivers mainly in the form of intelligent vehicle systems. Driver fatigue is a significant factor in a large number of vehicle accidents. Thus, driver drowsiness detection has been considered a major potential area so as to prevent a huge number of sleep induced road accidents. This paper proposes a vision based intelligent algorithm to detect driver drowsiness. Previous approaches are generally based on blink rate, eye closure, yawning, eye brow shape and other hand engineered facial features. The proposed algorithm makes use of features learnt using convolutional neural network so as to explicitly capture various latent facial features and the complex non-linear feature interactions. A softmax layer is used to classify the driver as drowsy or non-drowsy. This system is hence used for warning the driver of drowsiness or in attention to prevent traffic accidents. We present both qualitative and quantitative results to substantiate the claims made in the paper.

Erratum: The Belle II Physics Book (Progress of Theoretical and Experimental Physics (2019) 2019 (123C01) DOI: 10.1093/ptep/ptz106)

Abstract: In the original version of this manuscript, an error was introduced on pp352. '2.7nb:1.6nb' has been corrected to '2.4nb:1.3nb' in the current online and printed version. doi:10.1093/ptep/ptz106.

Biobutanol: science, engineering, and economics

Abstract: SUMMARY Among several liquid alternative fuels, biobutanol has shown great promise because of its very similar properties to gasoline. This review provides an overview of research activities in acetone–butanol–ethanol (ABE) fermentation over the past two and a half decades. We have addressed seven important facets of ABE fermentation, viz. biochemistry, microbial cultures, alternative substrates, solvent recovery, fermentation mode and reactor designs, mathematical modeling, and economics. Development of mutant strains having higher yield, selectivity and tolerance to inhibition, and search for cheap alternative substrates for fermentation are most important thrust areas in biobutanol production. New and efficient processes have been developed for in situ removal and recovery of the ABE solvents. Several rigorous kinetic and physiological models for fermentation have been formulated, which form useful tool for optimization of the process. These research activities have been reviewed in this paper. Finally, we have summarized studies on the economic viability of large-scale ABE fermentation processes employing various process designs, substrates, and microbial cultures. With the use of new strains, inexpensive substrates, and superior reactor designs, economic potential of ABE fermentation has been found to be highly attractive. Research efforts in science, engineering, and economics of ABE fermentation have brought biobutanol close to commercialization as liquid alternate fuel. Copyright © 2011 John Wiley & Sons, Ltd.

Rotate to Attend: Convolutional Triplet Attention Module

Abstract: Benefiting from the capability of building inter-dependencies among channels or spatial locations, attention mechanisms have been extensively studied and broadly used in a variety of computer vision tasks recently. In this paper, we investigate light-weight but effective attention mechanisms and present triplet attention, a novel method for computing attention weights by capturing cross-dimension interaction using a three-branch structure. For an input tensor, triplet attention builds inter-dimensional dependencies by the rotation operation followed by residual transformations and encodes inter-channel and spatial information with negligible computational overhead. Our method is simple as well as efficient and can be easily plugged into classic backbone networks as an add-on module. We demonstrate the effectiveness of our method on various challenging tasks including image classification on ImageNet-1k and object detection on MSCOCO and PASCAL VOC datasets. Furthermore, we provide extensive in-sight into the performance of triplet attention by visually inspecting the GradCAM and GradCAM++ results. The empirical evaluation of our method supports our intuition on the importance of capturing dependencies across dimensions when computing attention weights. Code for this paper can be publicly accessed at this https URL