scholarly journals Analysis of Heat Dissipation and Reliability in Information Erasure: A Gaussian Mixture Approach

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 749 ◽  
Author(s):  
Saurav Talukdar ◽  
Shreyas Bhaban ◽  
James Melbourne ◽  
Murti Salapaka

This article analyzes the effect of imperfections in physically realizable memory. Motivated by the realization of a bit as a Brownian particle within a double well potential, we investigate the energetics of an erasure protocol under a Gaussian mixture model. We obtain sharp quantitative entropy bounds that not only give rigorous justification for heuristics utilized in prior works, but also provide a guide toward the minimal scale at which an erasure protocol can be performed. We also compare the results obtained with the mean escape times from double wells to ensure reliability of the memory. The article quantifies the effect of overlap of two Gaussians on the the loss of interpretability of the state of a one bit memory, the required heat dissipated in partially successful erasures and reliability of information stored in a memory bit.

2021 ◽  
Author(s):  
Kehinde Lydia Ajayi ◽  
Victor Azeta ◽  
Isaac Odun-Ayo ◽  
Ambrose Azeta ◽  
Ajayi Peter Taiwo ◽  
...  

Abstract One of the current research areas is speech recognition by aiding in the recognition of speech signals through computer applications. In this research paper, Acoustic Nudging, (AN) Model is used in re-formulating the persistence automatic speech recognition (ASR) errors that involves user’s acoustic irrational behavior which alters speech recognition accuracy. GMM helped in addressing low-resourced attribute of Yorùbá language to achieve better accuracy and system performance. From the simulated results given, it is observed that proposed Acoustic Nudging-based Gaussian Mixture Model (ANGM) improves accuracy and system performance which is evaluated based on Word Recognition Rate (WRR) and Word Error Rate (WER)given by validation accuracy, testing accuracy, and training accuracy. The evaluation results for the mean WRR accuracy achieved for the ANGM model is 95.277% and the mean Word Error Rate (WER) is 4.723%when compared to existing models. This approach thereby reduce error rate by 1.1%, 0.5%, 0.8%, 0.3%, and 1.4% when compared with other models. Therefore this work was able to discover a foundation for advancing current understanding of under-resourced languages and at the same time, development of accurate and precise model for speech recognition.


2018 ◽  
Vol 30 (4) ◽  
pp. 642
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Qing Zhang ◽  
Xiaojie Shi ◽  
...  

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