870 research outputs found
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Leadership capability of team leaders in construction industry
This research was conducted to identify the important leadership capabilities for
Malaysia construction industry team leaders. This research used exploratory sequential
mix-method research design which is qualitative followed by quantitative research
method. In the qualitative phase, semi-structured in-depth interview was selected
and purposive sampling was employed in selecting 15 research participants involving
team leaders and Human Resource Managers. Qualitative data was analysed using
content and thematic analyses. Quantitative data was collected using survey
questionnaire involving 171 randomly selected team leaders as respondents. The data
was analyzed using descriptive and inferential statistics consisting of t-test, One-way
Analysis of Variance (ANOVA), Pearson Correlation, Multiple Regression and
Structured Equation Modeling (SEM). This study found that personal integrity, working
within industry, customer focus and quality, communication and interpersonal skill,
developing and empowering people and working as a team were needed leadership
capabilities among construction industry team leaders. The research was also able to
prove that leadership skill is a key element to develop leadership capability. A
framework was developed based on the results of this study, which can be used as a
guide by employers and relevant agencies in enhancing leadership capability of
Malaysia construction industry team leade
Feature Extracting in the Presence of Environmental Noise, using Subband Adaptive Filtering
In this work, a new feature extracting method in noisy environments is proposed. The approach is based on subband decomposition of speech signals followed by adaptive filtering in the noisiest subbbands of speech. The speech decomposition is obtained using low complexity octave filter bank, while adaptive filtering is performed using the normalized least mean square algorithm. The performance of the new feature was evaluated for isolated word speech recognition in the presence of a car noise. The proposed method showed higher recognition accuracy than conventional methods in noisy environments
Predicting Audio Advertisement Quality
Online audio advertising is a particular form of advertising used abundantly
in online music streaming services. In these platforms, which tend to host tens
of thousands of unique audio advertisements (ads), providing high quality ads
ensures a better user experience and results in longer user engagement.
Therefore, the automatic assessment of these ads is an important step toward
audio ads ranking and better audio ads creation. In this paper we propose one
way to measure the quality of the audio ads using a proxy metric called Long
Click Rate (LCR), which is defined by the amount of time a user engages with
the follow-up display ad (that is shown while the audio ad is playing) divided
by the impressions. We later focus on predicting the audio ad quality using
only acoustic features such as harmony, rhythm, and timbre of the audio,
extracted from the raw waveform. We discuss how the characteristics of the
sound can be connected to concepts such as the clarity of the audio ad message,
its trustworthiness, etc. Finally, we propose a new deep learning model for
audio ad quality prediction, which outperforms the other discussed models
trained on hand-crafted features. To the best of our knowledge, this is the
first large-scale audio ad quality prediction study.Comment: WSDM '18 Proceedings of the Eleventh ACM International Conference on
Web Search and Data Mining, 9 page
Audio Coding Based on Integer Transforms
Die Audiocodierung hat sich in den letzten Jahren zu einem sehr
populÀren Forschungs- und Anwendungsgebiet entwickelt. Insbesondere
gehörangepasste Verfahren zur Audiocodierung, wie etwa MPEG-1 Layer-3
(MP3) oder MPEG-2 Advanced Audio Coding (AAC), werden hÀufig zur
effizienten Speicherung und Ăbertragung von Audiosignalen verwendet. FĂŒr
professionelle Anwendungen, wie etwa die Archivierung und Ăbertragung im
Studiobereich, ist hingegen eher eine verlustlose Audiocodierung angebracht.
Die bisherigen AnsĂ€tze fĂŒr gehörangepasste und verlustlose
Audiocodierung sind technisch völlig verschieden. Moderne
gehörangepasste Audiocoder basieren meist auf FilterbÀnken, wie etwa der
ĂŒberlappenden orthogonalen Transformation "Modifizierte Diskrete
Cosinus-Transformation" (MDCT). Verlustlose Audiocoder hingegen
verwenden meist prÀdiktive Codierung zur Redundanzreduktion. Nur wenige
AnsÀtze zur transformationsbasierten verlustlosen Audiocodierung wurden
bisher versucht.
Diese Arbeit prÀsentiert einen neuen Ansatz hierzu, der das
Lifting-Schema auf die in der gehörangepassten Audiocodierung
verwendeten ĂŒberlappenden Transformationen anwendet. Dies ermöglicht
eine invertierbare Integer-Approximation der ursprĂŒnglichen
Transformation, z.B. die IntMDCT als Integer-Approximation der MDCT. Die
selbe Technik kann auch fĂŒr FilterbĂ€nke mit niedriger Systemverzögerung
angewandt werden. Weiterhin ermöglichen ein neuer, mehrdimensionaler
Lifting-Ansatz und eine Technik zur Spektralformung von
Quantisierungsfehlern eine Verbesserung der Approximation der
ursprĂŒnglichen Transformation.
Basierend auf diesen neuen Integer-Transformationen werden in dieser
Arbeit neue Verfahren zur Audiocodierung vorgestellt. Die Verfahren
umfassen verlustlose Audiocodierung, eine skalierbare verlustlose
Erweiterung eines gehörangepassten Audiocoders und einen integrierten
Ansatz zur fein skalierbaren gehörangepassten und verlustlosen
Audiocodierung. SchlieĂlich wird mit Hilfe der Integer-Transformationen
ein neuer Ansatz zur unhörbaren Einbettung von Daten mit hohen
Datenraten in unkomprimierte Audiosignale vorgestellt.In recent years audio coding has become a very popular field for
research and applications. Especially perceptual audio coding schemes,
such as MPEG-1 Layer-3 (MP3) and MPEG-2 Advanced Audio Coding (AAC), are
widely used for efficient storage and transmission of music
signals. Nevertheless, for professional applications, such as archiving
and transmission in studio environments, lossless audio coding schemes
are considered more appropriate.
Traditionally, the technical approaches used in perceptual and lossless
audio coding have been separate worlds. In perceptual audio coding, the
use of filter banks, such as the lapped orthogonal transform "Modified
Discrete Cosine Transform" (MDCT), has been the approach of choice being
used by many state of the art coding schemes. On the other hand,
lossless audio coding schemes mostly employ predictive coding of
waveforms to remove redundancy. Only few attempts have been made so far
to use transform coding for the purpose of lossless audio coding.
This work presents a new approach of applying the lifting scheme to
lapped transforms used in perceptual audio coding. This allows for an
invertible integer-to-integer approximation of the original transform,
e.g. the IntMDCT as an integer approximation of the MDCT. The same
technique can also be applied to low-delay filter banks. A generalized,
multi-dimensional lifting approach and a noise-shaping technique are
introduced, allowing to further optimize the accuracy of the
approximation to the original transform.
Based on these new integer transforms, this work presents new audio
coding schemes and applications. The audio coding applications cover
lossless audio coding, scalable lossless enhancement of a perceptual
audio coder and fine-grain scalable perceptual and lossless audio
coding. Finally an approach to data hiding with high data rates in
uncompressed audio signals based on integer transforms is described
Audio Coding Based on Integer Transforms
Die Audiocodierung hat sich in den letzten Jahren zu einem sehr
populÀren Forschungs- und Anwendungsgebiet entwickelt. Insbesondere
gehörangepasste Verfahren zur Audiocodierung, wie etwa MPEG-1 Layer-3
(MP3) oder MPEG-2 Advanced Audio Coding (AAC), werden hÀufig zur
effizienten Speicherung und Ăbertragung von Audiosignalen verwendet. FĂŒr
professionelle Anwendungen, wie etwa die Archivierung und Ăbertragung im
Studiobereich, ist hingegen eher eine verlustlose Audiocodierung angebracht.
Die bisherigen AnsĂ€tze fĂŒr gehörangepasste und verlustlose
Audiocodierung sind technisch völlig verschieden. Moderne
gehörangepasste Audiocoder basieren meist auf FilterbÀnken, wie etwa der
ĂŒberlappenden orthogonalen Transformation "Modifizierte Diskrete
Cosinus-Transformation" (MDCT). Verlustlose Audiocoder hingegen
verwenden meist prÀdiktive Codierung zur Redundanzreduktion. Nur wenige
AnsÀtze zur transformationsbasierten verlustlosen Audiocodierung wurden
bisher versucht.
Diese Arbeit prÀsentiert einen neuen Ansatz hierzu, der das
Lifting-Schema auf die in der gehörangepassten Audiocodierung
verwendeten ĂŒberlappenden Transformationen anwendet. Dies ermöglicht
eine invertierbare Integer-Approximation der ursprĂŒnglichen
Transformation, z.B. die IntMDCT als Integer-Approximation der MDCT. Die
selbe Technik kann auch fĂŒr FilterbĂ€nke mit niedriger Systemverzögerung
angewandt werden. Weiterhin ermöglichen ein neuer, mehrdimensionaler
Lifting-Ansatz und eine Technik zur Spektralformung von
Quantisierungsfehlern eine Verbesserung der Approximation der
ursprĂŒnglichen Transformation.
Basierend auf diesen neuen Integer-Transformationen werden in dieser
Arbeit neue Verfahren zur Audiocodierung vorgestellt. Die Verfahren
umfassen verlustlose Audiocodierung, eine skalierbare verlustlose
Erweiterung eines gehörangepassten Audiocoders und einen integrierten
Ansatz zur fein skalierbaren gehörangepassten und verlustlosen
Audiocodierung. SchlieĂlich wird mit Hilfe der Integer-Transformationen
ein neuer Ansatz zur unhörbaren Einbettung von Daten mit hohen
Datenraten in unkomprimierte Audiosignale vorgestellt.In recent years audio coding has become a very popular field for
research and applications. Especially perceptual audio coding schemes,
such as MPEG-1 Layer-3 (MP3) and MPEG-2 Advanced Audio Coding (AAC), are
widely used for efficient storage and transmission of music
signals. Nevertheless, for professional applications, such as archiving
and transmission in studio environments, lossless audio coding schemes
are considered more appropriate.
Traditionally, the technical approaches used in perceptual and lossless
audio coding have been separate worlds. In perceptual audio coding, the
use of filter banks, such as the lapped orthogonal transform "Modified
Discrete Cosine Transform" (MDCT), has been the approach of choice being
used by many state of the art coding schemes. On the other hand,
lossless audio coding schemes mostly employ predictive coding of
waveforms to remove redundancy. Only few attempts have been made so far
to use transform coding for the purpose of lossless audio coding.
This work presents a new approach of applying the lifting scheme to
lapped transforms used in perceptual audio coding. This allows for an
invertible integer-to-integer approximation of the original transform,
e.g. the IntMDCT as an integer approximation of the MDCT. The same
technique can also be applied to low-delay filter banks. A generalized,
multi-dimensional lifting approach and a noise-shaping technique are
introduced, allowing to further optimize the accuracy of the
approximation to the original transform.
Based on these new integer transforms, this work presents new audio
coding schemes and applications. The audio coding applications cover
lossless audio coding, scalable lossless enhancement of a perceptual
audio coder and fine-grain scalable perceptual and lossless audio
coding. Finally an approach to data hiding with high data rates in
uncompressed audio signals based on integer transforms is described
Recommended from our members
AZIP, audio compression system: Research on audio compression, comparison of psychoacoustic principles and genetic algorithms
The purpose of this project is to investigate the differences between psychoacoustic principles and genetic algorithms (GA0). These will be discussed separately. The review will also compare the compression ratio and the quality of the decompressed files decoded by these two methods
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