The Application of the Continuous Anodic Oxidation Technique for the Evaluation of State-of-the-Art Front-End Structures

In this study, we evaluate and compare two different approaches for multilingual phone recognition in code-switched and non-code-switched scenarios. First approach is a front-end Language Identification (LID)-switched to a monolingual phone recognizer (LID-Mono), trained individually on each of the languages present in multilingual dataset. In the second approach, a common multilingual phone-set derived from the International Phonetic Alphabet (IPA) transcription of the multilingual dataset is used to develop a Multilingual Phone Recognition System (Multi-PRS). The bilingual code-switching experiments are conducted using Kannada and Urdu languages. In the first approach, LID is performed using the state-of-the-art i-vectors. Both monolingual and multilingual phone recognition systems are trained using Deep Neural Networks. The performance of LID-Mono and Multi-PRS approaches are compared and analysed in detail. It is found that the performance of Multi-PRS approach is superior compared to more conventional LID-Mono approach in both code-switched and non-code-switched scenarios. For code-switched speech, the effect of length of segments (that are used to perform LID) on the performance of LID-Mono system is studied by varying the window size from 500 ms to 5.0 s, and full utterance. The LID-Mono approach heavily depends on the accuracy of the LID system and the LID errors cannot be recovered. But, the Multi-PRS system by virtue of not having to do a front-end LID switching and designed based on the common multilingual phone-set derived from several languages, is not constrained by the accuracy of the LID system, and hence performs effectively on code-switched and non-code-switched speech, offering low Phone Error Rates than the LID-Mono system.

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Front-end implications to multi-standard cellular radios: State-of-the-art and future trends

2010 IEEE International Ultrasonics Symposium ◽

10.1109/ultsym.2010.5935487 ◽

2010 ◽

Cited By ~ 4

Author(s):

Rastislav Vazny ◽

Harald Pretl ◽

Linus Maurer ◽

Robert Weigel

Keyword(s):

State Of The Art ◽

Future Trends ◽

Front End

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Theoretical Analysis of Overlay GNSS Receiver Effects

International Journal of Embedded and Real-Time Communication Systems ◽

10.4018/jertcs.2012070103 ◽

2012 ◽

Vol 3 (3) ◽

pp. 38-53

Author(s):

Alexander Rügamer ◽

Cécile Mongrédien ◽

Santiago Urquijo ◽

Günter Rohmer

Keyword(s):

Lower Bound ◽

State Of The Art ◽

Optimal Path ◽

Signal Quality ◽

Relative Gain ◽

Noise Floor ◽

Code Tracking ◽

Front End ◽

Path Control ◽

Multi Band

Having given a short overview of GNSS signals and state-of-the-art multi-band front-end architectures, this paper presents a novel contribution to efficient multi-band GNSS reception. A general overlay based front-end architecture is introduced that enables the joint reception of two signals broadcast in separate frequency bands, sharing just one common baseband stage. The consequences of this overlay are analyzed for both signal and noise components. Signal overlay is shown to have a negligible impact on signal quality. It is shown that the noise floor superposition results in non-negligible degradations. However, it is also demonstrated that these degradations can be minimized by judiciously setting the relative gain between the two signal paths. As an illustration, the analytical optimal path-control expression to combine overlaid signals in an ionospheric-free pseudorange is derived for both Cramér-Rao Lower Bound and practical code tracking parameters. Finally, some practical overlay receiver and path control aspects are discussed.

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Speaker Awareness for Speech Emotion Recognition

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v16i04.11870 ◽

2020 ◽

Vol 16 (04) ◽

pp. 15 ◽

Cited By ~ 1

Author(s):

Gustavo Assunção ◽

Paulo Menezes ◽

Fernando Perdigão

Keyword(s):

Machine Learning ◽

State Of The Art ◽

Considerable Improvement ◽

Speech Emotion Recognition ◽

Emotional States ◽

Emotional Prosody ◽

Emotional Speech ◽

Front End ◽

Machine Learning Model

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>The idea of recognizing human emotion through speech (SER) has recently received considerable attention from the research community, mostly due to the current machine learning trend. Nevertheless, even the most successful methods are still rather lacking in terms of adaptation to specific speakers and scenarios, evidently reducing their performance when compared to humans. In this paper, we evaluate a largescale machine learning model for classification of emotional states. This model has been trained for speaker iden- tification but is instead used here as a front-end for extracting robust features from emotional speech. We aim to verify that SER improves when some speak- er</span><span>’</span><span>s emotional prosody cues are considered. Experiments using various state-of- the-art classifiers are carried out, using the Weka software, so as to evaluate the robustness of the extracted features. Considerable improvement is observed when comparing our results with other SER state-of-the-art techniques.</span></p></div></div></div>

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The Continuous Anodic Oxidation Technique

10.1063/1.2799383 ◽

2007 ◽

Cited By ~ 3

Author(s):

S. Prussin ◽

David G. Seiler ◽

Alain C. Diebold ◽

Robert McDonald ◽

C. Michael Garner ◽

...

Keyword(s):

Anodic Oxidation ◽

Anodic Oxidation Technique

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Identifying GNSS Signals Based on Their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator

Data ◽

10.3390/data5010018 ◽

2020 ◽

Vol 5 (1) ◽

pp. 18

Author(s):

Ruben Morales-Ferre ◽

Wenbo Wang ◽

Alejandro Sanz-Abia ◽

Elena-Simona Lohan

Keyword(s):

Radio Frequency ◽

Antenna Array ◽

Classification Accuracy ◽

State Of The Art ◽

Data Use ◽

General Purpose ◽

Receiver Side ◽

Raw Data ◽

Front End ◽

Signal Type

This is a data descriptor paper for a set of raw GNSS signals collected via roof antennas and Spectracom simulator for general-purpose uses. We give one example of possible data use in the context of Radio Frequency Fingerprinting (RFF) studies for signal-type identification based on front-end hardware characteristics at transmitter or receiver side. Examples are given in this paper of achievable classification accuracy of six of the collected signal classes. The RFF is one of the state-of-the-art, promising methods to identify GNSS transmitters and receivers, and can find future applicability in anti-spoofing and anti-jamming solutions for example. The uses of the provided raw data are not limited to RFF studies, but can extend to uses such as testing GNSS acquisition and tracking, antenna array experiments, and so forth.

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Preparation of Fe-Pt Nanowires through Anodic Oxidation of Sputtered Aluminum on Glass Surface

MRS Proceedings ◽

10.1557/proc-788-l8.4 ◽

2003 ◽

Vol 788 ◽

Cited By ~ 3

Author(s):

Satoru Inoue ◽

Song-Zhu Chu ◽

Kenji Wada ◽

Yasushi Kanke

Keyword(s):

Thin Film ◽

Anodic Oxidation ◽

Glass Surface ◽

Anodic Alumina ◽

Alumina Layer ◽

Electro Deposition ◽

New Process ◽

High Density Magnetic Recording ◽

Anodic Oxidation Technique ◽

Pt Nanowires

ABSTRACTNew process for the preparation of Fe-Pt nanowires has been developed through anodic oxidation and electro deposition technique. Aluminum thin film sputtered on ITO thin film on a glass surface was decomposed into alumina by anodic oxidation technique. The anodic alumina layer possessed nanometer size pore array standing on the glass surface. The barrier layer at the bottom of nanopores was removed by acid etching to attain DC field smooth electro deposition. Fe-Pt components were introduced into nanopores of anodic alumina by electro deposition. The magnetization of Fe-Pt nanowires was investigated. The magnetization perpendicular to the glass surface was very strong and the in-plane magnetization was very small, indicating that the magnetic Fe-Pt nanowires could be potentially applied to ultra high density magnetic recording. The density of the nanowires was estimated to be about 1T bits /inch2.

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Improvement of EEG Signal Acquisition: An Electrical Aspect for State of the Art of Front End

Computational Intelligence and Neuroscience ◽

10.1155/2010/630649 ◽

2010 ◽

Vol 2010 ◽

pp. 1-7 ◽

Cited By ~ 39

Author(s):

Ali Bulent Usakli

Keyword(s):

State Of The Art ◽

Signal Acquisition ◽

Electronic Noise ◽

Eeg Signal ◽

Signal Conversion ◽

Electronic Circuitry ◽

Front End ◽

Recording Session ◽

Reduction Methods ◽

Electroencephalogram Eeg

The aim of this study is to present some practical state-of-the-art considerations in acquiring satisfactory signals for electroencephalographic signal acquisition. These considerations are important for users and system designers. Especially choosing correct electrode and design strategy of the initial electronic circuitry front end plays an important role in improving the system's measurement performance. Considering the pitfalls in the design of biopotential measurement system and recording session conditions creates better accuracy. In electroencephalogram (EEG) recording electrodes, system electronics including filtering, amplifying, signal conversion, data storing, and environmental conditions affect the recording performance. In this paper, EEG electrode principles and main points of electronic noise reduction methods in EEG signal acquisition front end are discussed, and some suggestions for improving signal acquisition are presented.

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