Speech Interface for Controlling Micro Air Vehicle

2020 ◽  
Vol 17 (1) ◽  
pp. 488-491
Author(s):  
P. Lakshmi ◽  
S. Veena ◽  
D. K. Rahul ◽  
H. Lokesha
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Automatic Speech Recognition ◽  
Micro Air Vehicle ◽  
The Other ◽  
Ground Control ◽  
Control Applications ◽  
Speech Interface ◽  
Air Vehicle ◽  
Ground Control Station

This paper focuses on the development of the speech interface for controlling a Micro Air Vehicle (MAV). A speech interface in such control applications will have two distinct modules. One is the Automatic Speech Recognition (ASR) module and the other is the Natural Language Processing (NLP) module. The ASR is developed using the models built using CMU Sphinx toolkit. The NLP scheme is proposed and developed using Natural Language Toolkit (NLTK). Understanding of the speech is very important in such kind of control applications. The NLP outcome is used to invoke the Ground Control Station (GCS) commands. The results are validated in a Flight Gear simulator using Mission Planner GCS configured for MAV.

scholarly journals A Practical Deployment of a Communication Infrastructure to Support the Employment of Multiple Surveillance Drones Systems

Drones ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 26 ◽  
Author(s):  
Maik Basso ◽  
Iulisloi Zacarias ◽  
Carlos Tussi Leite ◽  
Haijun Wang ◽  
Edison Pignaton de Freitas
Keyword(s):  
Surveillance System ◽  
Micro Air Vehicle ◽  
Ground Control ◽  
Network Infrastructure ◽  
Communication Infrastructure ◽  
Air Vehicle ◽  
Ground Control Station ◽  
And Control

In many incidents involving amateur drones (ADr), the big challenge is to quickly deploy a surveillance system that countermeasures the threat and keeps track of the intruders. Depending on the area under concern, launching a single surveillance drone (SDr) to hunt the intruder is not efficient, but employing multiple ones can cope with the problem. However, in order to make this approach feasible, an easy to use mission setup and control station for multiple SDr is required, which by its turn, requires a communication infrastructure able to handle the connection of multiple SDr among themselves and their ground control and payload visualization station. Concerning this Issue, this paper presents a proposal of a network infrastructure to support the operation of multiple SDr and its practical deployment. This infrastructure extends the existing Micro Air Vehicle Link (MAVLink) protocol to support multiple connections among the SDrs and between them and a ground control station. Encouraging results are obtained, showing the viability of this proposed protocol extension.

Research on Sustainable Mining Engineering

2013 ◽  
Vol 340 ◽  
pp. 126-130 ◽  
Author(s):  
Xiao Guang Yue ◽  
Guang Zhang ◽  
Qing Guo Ren ◽  
Wen Cheng Liao ◽  
Jing Xi Chen ◽  
...  
Keyword(s):  
Information Processing ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Social Economy ◽  
Scientific Research ◽  
The Other ◽  
Mining Engineering ◽  
Chinese Information Processing ◽  
Development Tendency

The concepts of Chinese information processing and natural language processing (NLP) and their development tendency are summarized. There are different comprehension of Chinese information processing and natural language processing in China and the other countries. But the work appears to emerge in the study of key point of languages processing. Mining engineering is very important for our country. Though the final task of languages processing is difficult, Chinese information processing has contributed substantially to our scientific research and social economy and it will play an important part for mining engineering in our future.

scholarly journals Multi-Sense Embeddings per Word

2020 ◽  
Author(s):  
Masashi Sugiyama
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Research Area ◽  
Word Embedding ◽  
The Other ◽  
Word Embeddings ◽  
Word Similarity ◽  
Better Than ◽  
Non Parametric

Recently, word embeddings have been used in many natural language processing problems successfully and how to train a robust and accurate word embedding system efficiently is a popular research area. Since many, if not all, words have more than one sense, it is necessary to learn vectors for all senses of word separately. Therefore, in this project, we have explored two multi-sense word embedding models, including Multi-Sense Skip-gram (MSSG) model and Non-parametric Multi-sense Skip Gram model (NP-MSSG). Furthermore, we propose an extension of the Multi-Sense Skip-gram model called Incremental Multi-Sense Skip-gram (IMSSG) model which could learn the vectors of all senses per word incrementally. We evaluate all the systems on word similarity task and show that IMSSG is better than the other models.

scholarly journals Natural language inference for Malayalam language using language agnostic sentence representation

2021 ◽  
Vol 7 ◽  
pp. e508
Author(s):  
Sara Renjit ◽  
Sumam Idicula
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Binary Classification ◽  
Multiclass Classification ◽  
The Other ◽  
Indian Language ◽  
Information Retrieval Systems ◽  
Textual Entailment ◽  
Malayalam Language

Natural language inference (NLI) is an essential subtask in many natural language processing applications. It is a directional relationship from premise to hypothesis. A pair of texts is defined as entailed if a text infers its meaning from the other text. The NLI is also known as textual entailment recognition, and it recognizes entailed and contradictory sentences in various NLP systems like Question Answering, Summarization and Information retrieval systems. This paper describes the NLI problem attempted for a low resource Indian language Malayalam, the regional language of Kerala. More than 30 million people speak this language. The paper is about the Malayalam NLI dataset, named MaNLI dataset, and its application of NLI in Malayalam language using different models, namely Doc2Vec (paragraph vector), fastText, BERT (Bidirectional Encoder Representation from Transformers), and LASER (Language Agnostic Sentence Representation). Our work attempts NLI in two ways, as binary classification and as multiclass classification. For both the classifications, LASER outperformed the other techniques. For multiclass classification, NLI using LASER based sentence embedding technique outperformed the other techniques by a significant margin of 12% accuracy. There was also an accuracy improvement of 9% for LASER based NLI system for binary classification over the other techniques.

scholarly journals Natural Language Processing in Serious Games: A state of the art.

2015 ◽  
Vol 2 (3) ◽  
Author(s):  
Davide Picca ◽  
Dominique Jaccard ◽  
Gérald Eberlé
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Serious Games ◽  
State Of The Art ◽  
Serious Game ◽  
The Other ◽  
Other Hand ◽  
The One ◽  
High Level

In the last decades, Natural Language Processing (NLP) has obtained a high level of success. Interactions between NLP and Serious Games have started and some of them already include NLP techniques. The objectives of this paper are twofold: on the one hand, providing a simple framework to enable analysis of potential uses of NLP in Serious Games and, on the other hand, applying the NLP framework to existing Serious Games and giving an overview of the use of NLP in pedagogical Serious Games. In this paper we present 11 serious games exploiting NLP techniques. We present them systematically, according to the following structure:  first, we highlight possible uses of NLP techniques in Serious Games, second, we describe the type of NLP implemented in the each specific Serious Game and, third, we provide a link to possible purposes of use for the different actors interacting in the Serious Game.

Researching in Shallow Parsing Based on Function Invariance of Maximum Likelihood Estimation

2013 ◽  
Vol 274 ◽  
pp. 359-362
Author(s):  
Shuang Zhang ◽  
Shi Xiong Zhang
Keyword(s):  
Natural Language Processing ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Natural Language ◽  
Language Processing ◽  
Likelihood Estimation ◽  
The Other ◽  
Applied Technology ◽  
New Strategy ◽  
Shallow Parsing

Abstract. Shallow parsing is a new strategy of language processing in the domain of natural language processing recently years. It is not focus on the obtaining of the full parsing tree but requiring of the recognition of some simple composition of some structure. It separated parsing into two subtasks: one is the recognition and analysis of chunks the other is the analysis of relationships among chunks. In this essay, some applied technology of shallow parsing is introduced and a new method of it is experimented.

scholarly journals Sentiment Analysis of Review Data of a Product Using Python

2018 ◽  
Vol 7 (3.12) ◽  
pp. 674
Author(s):  
P Santhi Priya ◽  
T Venkateswara Rao
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
The Other ◽  
Product Reviews ◽  
Prime Problem

The other name of sentiment analysis is the opinion mining. It’s one of the primary objectives in a Natural Language Processing(NLP). Opinion mining is having a lot of audience lately. In our research we have taken up a prime problem of opinion mining which is theSentiment Polarity Categorization(SPC) that is very influential. We proposed a methodology for the SPC with explanations to the minute level. Apart from theories computations are made on both review standard and sentence standard categorization with benefitting outcomes. Also, the data that is represented here is from the product reviews given on the shopping site called Amazon.  

scholarly journals NMT Multi-Sense Embeddings per Word

2019 ◽  
Author(s):  
William Jin
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Research Area ◽  
Word Embedding ◽  
The Other ◽  
Word Embeddings ◽  
Word Similarity ◽  
Better Than ◽  
Non Parametric

Recently, word embeddings have been used in many natural language processing problems successfully and how to train a robust and accurate word embedding system efficiently is a popular research area. Since many, if not all, words have more than one sense, it is necessary to learn vectors for all senses of word separately. Therefore, in this project, we have explored two multi-sense word embedding models, including Multi-Sense Skip-gram (MSSG) model and Non-parametric Multi-sense Skip Gram model (NP-MSSG). Furthermore, we propose an extension of the Multi-Sense Skip-gram model called Incremental Multi-Sense Skip-gram (IMSSG) model which could learn the vectors of all senses per word incrementally. We evaluate all the systems on word similarity task and show that IMSSG is better than the other models.

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