arXiv subjects

Ashutosh Mishra

Publications and source records attributed to Ashutosh Mishra.

23 records · Page 2Linked to original sources

arXiv2023

Size-dependent Failure Behavior of Lithium-Iron Phosphate Battery under Mechanical Abuse

Under mechanical abuse conditions, the failure of lithium-ion batteries occurs in various stages characterized by different force, temperature and voltage response which require it's in-situ measurements for analysis. Firstly, four sizes of commercially available lithium-iron phosphate batteries (LFPB) viz. 18650, 22650, 26650, and 32650 are subjected to quasi static lateral, longitudinal compression, and nail penetration tests. The failure, characterized by the voltage drop and temperature rise, at the onset of the first internal short-circuit (ISC), is identified by Aurdino-based voltage sensor module and temperature measurement module, respectively. The battery failure load and peak temperature at the onset of ISC are found to rely on the battery size strongly. The failure is observed to be delayed for small-sized 18650 batteries during lateral compression, unlike longitudinal compression and nail penetration test. At the onset of the short circuit, the temperature rise above the ambient value is different for different LFPBs. It is found to be maximum (64.4 degree C ) for LFPB 32650 under longitudinal compression and minimum (29.5 degree C) under lateral compression tests amongst the considered geometries. Further, LFPB 26650 exhibited a balanced thermal behavior during the considered abused condition. Such data can be sensed timely for effective thermal management and improved safety of lithium-ion batteries.

cond-mat.mtrl-sci↗

arXiv2022

A Survey on Deep Reinforcement Learning-based Approaches for Adaptation and Generalization

Deep Reinforcement Learning (DRL) aims to create intelligent agents that can learn to solve complex problems efficiently in a real-world environment. Typically, two learning goals: adaptation and generalization are used for baselining DRL algorithm's performance on different tasks and domains. This paper presents a survey on the recent developments in DRL-based approaches for adaptation and generalization. We begin by formulating these goals in the context of task and domain. Then we review the recent works under those approaches and discuss future research directions through which DRL algorithms' adaptability and generalizability can be enhanced and potentially make them applicable to a broad range of real-world problems.

cs.LG↗

arXiv2019

Using Deep Object Features for Image Descriptions

Inspired by recent advances in leveraging multiple modalities in machine translation, we introduce an encoder-decoder pipeline that uses (1) specific objects within an image and their object labels, (2) a language model for decoding joint embedding of object features and the object labels. Our pipeline merges prior detected objects from the image and their object labels and then learns the sequences of captions describing the particular image. The decoder model learns to extract descriptions for the image from scratch by decoding the joint representation of the object visual features and their object classes conditioned by the encoder component. The idea of the model is to concentrate only on the specific objects of the image and their labels for generating descriptions of the image rather than visual feature of the entire image. The model needs to be calibrated more by adjusting the parameters and settings to result in better accuracy and performance.

cs.CV↗

arXiv2018

Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks

This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotations of any kind in these documents. The handwritten annotations can appear in form of underlines and text by using various writing instruments, e.g., the use of pencils makes the data more challenging. We train and evaluate various end-to-end semantic segmentation approaches and report the results. The task is to classify the pixels of documents into two classes: background and handwritten annotation. The best model achieves a mean Intersection over Union (IoU) score of 95.6% on the test documents of the presented dataset. We also present a comparison of different strategies used for data augmentation and training on our presented dataset. For evaluation, we use the Layout Analysis Evaluator for the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts.

cs.CV↗

arXiv2015

A Low Complexity VLSI Architecture for Multi-Focus Image Fusion in DCT Domain

Due to the confined focal length of optical sensors, focusing all objects in a scene with a single sensor is a difficult task. To handle such a situation, image fusion methods are used in multi-focus environment. Discrete Cosine Transform (DCT) is a widely used image compression transform, image fusion in DCT domain is an efficient method. This paper presents a low complexity approach for multi-focus image fusion and its VLSI implementation using DCT. The proposed method is evaluated using reference/non-reference fusion measure criteria and the obtained results asserts it's effectiveness. The maximum synthesized frequency on FPGA is found to be 221 MHz and consumes 42% of FPGA resources. The proposed method consumes very less power and can process 4K resolution images at the rate of 60 frames per second which makes the hardware suitable for handheld portable devices such as camera module and wireless image sensors.

cs.CV↗