arXiv subjects

Ankur Kumar

Publications and source records attributed to Ankur Kumar.

23 records · Page 2Linked to original sources

arXiv2022

LabVIEW is faster and C is economical interfacing tool for UCT automation

An in-house developed 2D ultrasound computerized Tomography system is fully automated. Performance analysis of instrument and software interfacing soft tools, namely the LabVIEW, MATLAB, C, and Python, is presented. The instrument interfacing algorithms, hardware control algorithms, signal processing, and analysis codes are written using above mentioned soft tool platforms. Total of eight performance indices are used to compare the ease of (a) realtime control of electromechanical assembly, (b) sensors, instruments integration, (c) synchronized data acquisition, and (d) simultaneous raw data processing. It is found that C utilizes the least processing power and performs a lower number of processes to perform the same task. In runtime analysis (data acquisition and realtime control), LabVIEW performs best, taking 365.69s in comparison to MATLAB (623.83s), Python ( 1505.54s), and C (1252.03s) to complete the experiment. Python performs better in establishing faster interfacing and minimum RAM usage. LabVIEW is recommended for its fast process execution. C is recommended for the most economical implementation. Python is recommended for complex system automation having a very large number of components involved. This article provides a methodology to select optimal soft tools for instrument automation-related aspects.

cs.PL↗

arXiv2022

Vision Transformer Compression with Structured Pruning and Low Rank Approximation

Transformer architecture has gained popularity due to its ability to scale with large dataset. Consequently, there is a need to reduce the model size and latency, especially for on-device deployment. We focus on vision transformer proposed for image recognition task (Dosovitskiy et al., 2021), and explore the application of different compression techniques such as low rank approximation and pruning for this purpose. Specifically, we investigate a structured pruning method proposed recently in Zhu et al. (2021) and find that mostly feedforward blocks are pruned with this approach, that too, with severe degradation in accuracy. We propose a hybrid compression approach to mitigate this where we compress the attention blocks using low rank approximation and use the previously mentioned pruning with a lower rate for feedforward blocks in each transformer layer. Our technique results in 50% compression with 14% relative increase in classification error whereas we obtain 44% compression with 20% relative increase in error when only pruning is applied. We propose further enhancements to bridge the accuracy gap but leave it as a future work.

cs.CV↗

arXiv2021

A review of on-device fully neural end-to-end automatic speech recognition algorithms

In this paper, we review various end-to-end automatic speech recognition algorithms and their optimization techniques for on-device applications. Conventional speech recognition systems comprise a large number of discrete components such as an acoustic model, a language model, a pronunciation model, a text-normalizer, an inverse-text normalizer, a decoder based on a Weighted Finite State Transducer (WFST), and so on. To obtain sufficiently high speech recognition accuracy with such conventional speech recognition systems, a very large language model (up to 100 GB) is usually needed. Hence, the corresponding WFST size becomes enormous, which prohibits their on-device implementation. Recently, fully neural network end-to-end speech recognition algorithms have been proposed. Examples include speech recognition systems based on Connectionist Temporal Classification (CTC), Recurrent Neural Network Transducer (RNN-T), Attention-based Encoder-Decoder models (AED), Monotonic Chunk-wise Attention (MoChA), transformer-based speech recognition systems, and so on. These fully neural network-based systems require much smaller memory footprints compared to conventional algorithms, therefore their on-device implementation has become feasible. In this paper, we review such end-to-end speech recognition models. We extensively discuss their structures, performance, and advantages compared to conventional algorithms.

cs.LG↗

arXiv2021

AI and conventional methods for UCT projection data estimation

A 2D Compact ultrasound computerized tomography (UCT) system is developed. Fully automatic post processing tools involving signal and image processing are developed as well. Square of the amplitude values are used in transmission mode with natural 1.5 MHz frequency and rise time 10.4 ns and fall time 8.4 ns and duty cycle of 4.32%. Highest peak to corresponding trough values are considered as transmitting wave between transducers in direct line talk. Sensitivity analysis of methods to extract peak to corresponding trough per transducer are discussed in this paper. Total five methods are tested. These methods are taken from broad categories: (a) Conventional and (b) Artificial Intelligence (AI) based methods. Conventional methods, namely: (a) simple gradient based peak detection, (b) Fourier based, (c) wavelet transform are compared with AI based methods: (a) support vector machine (SVM), (b) artificial neural network (ANN). Classification step was performed as well to discard the signal which does not has contribution of transmission wave. It is found that conventional methods have better performance. Reconstruction error, accuracy, F-Score, recall, precision, specificity and MCC for 40 x 40 data 1600 data files are measured. Each data file contains 50,002 data point. Ten such data files are used for training the Neural Network. Each data file has 7/8 wave packets and each packet corresponds to one transmission amplitude data. Reconstruction error is found to be minimum for ANN method. Other performance indices show that FFT method is processing the UCT signal with best recovery.

eess.SP↗

arXiv2019

Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models

In this paper, we propose a refined multi-stage multi-task training strategy to improve the performance of online attention-based encoder-decoder (AED) models. A three-stage training based on three levels of architectural granularity namely, character encoder, byte pair encoding (BPE) based encoder, and attention decoder, is proposed. Also, multi-task learning based on two-levels of linguistic granularity namely, character and BPE, is used. We explore different pre-training strategies for the encoders including transfer learning from a bidirectional encoder. Our encoder-decoder models with online attention show 35% and 10% relative improvement over their baselines for smaller and bigger models, respectively. Our models achieve a word error rate (WER) of 5.04% and 4.48% on the Librispeech test-clean data for the smaller and bigger models respectively after fusion with long short-term memory (LSTM) based external language model (LM).

eess.AS↗