2,581 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
Advances in Piezoelectric Transducers
The piezoelectric transducer converts electric signals into mechanical vibrations or vice versa by utilizing the morphological change of a crystal which occurs on voltage application, or conversely by monitoring the voltage generated by a pressure applied on a crystal. This book reports on the state of the art research and development findings on this very broad matter through original and innovative research studies exhibiting various investigation directions. The present book is a result of contributions of experts from international scientific community working in different aspects of piezoelectric transducers. The text is addressed not only to researchers, but also to professional engineers, students and other experts in a variety of disciplines, both academic and industrial seeking to gain a better understanding of what has been done in the field recently, and what kind of open problems are in this area
Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems
Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries
Should Conductors Listen To Recordings When They Learn Scores?
We can observe the conductor’s work at his performances and rehearsals. However, we cannot see the process of score preparation from the time they get the score to the downbeat of the first rehearsal because the conductor does this work alone. This dissertation discusses the debate among conductors past and present about whether, when, and how to use recordings as part of the score preparation. It considers the advantages and disadvantages of using recordings in the score preparation process. I conducted several types of research on the use of recordings by living conductors. I interviewed conductors using Skype and phone conversations, email correspondence, and in-person interviews. I found information online, and in books and magazine articles. This dissertation recounts these conductors’ methods of learning scores with and without listening to recordings and answers the following questions:
What are conductors specifically interested in when listening to recordings?
How did conductors from the past learn the scores when the recordings did not exist, or when they were not widely available?
Do conductors use recordings to learn the score?
How do conductors use the recordings in their score learning process?
The research presented within begins to answer the questions about learning scores, an important process for all conductors
Arctic Domain Awareness Center DHS Center of Excellence (COE): Project Work Plan
As stated by the DHS Science &Technology Directorate, “The increased and diversified use of maritime
spaces in the Arctic - including oil and gas exploration, commercial activities, mineral speculation, and
recreational activities (tourism) - is generating new challenges and risks for the U.S. Coast Guard and
other DHS maritime missions.” Therefore, DHS will look towards the new ADAC for research to
identify better ways to create transparency in the maritime domain along coastal regions and inland
waterways, while integrating information and intelligence among stakeholders. DHS expects the ADAC
to develop new ideas to address these challenges, provide a scientific basis, and develop new approaches
for U.S. Coast Guard and other DHS maritime missions. ADAC will also contribute towards the
education of both university students and mid-career professionals engaged in maritime security.
The US is an Arctic nation, and the Arctic environment is dynamic. We have less multi-year ice and more
open water during the summer causing coastal villages to experience unprecedented storm surges and
coastal erosion. Decreasing sea ice is also driving expanded oil exploration, bringing risks of oil spills.
Tourism is growing rapidly, and our fishing fleet and commercial shipping activities are increasing as
well. There continues to be anticipation of an economic pressure to open up a robust northwest passage
for commercial shipping. To add to the stresses of these changes is the fact that these many varied
activities are spread over an immense area with little connecting infrastructure. The related maritime
security issues are many, and solutions demand increasing maritime situational awareness and improved
crisis response capabilities, which are the focuses of our Work Plan.
UAA understands the needs and concerns of the Arctic community. It is situated on Alaska’s Southcentral
coast with the port facility through which 90% of goods for Alaska arrive. It is one of nineteen US
National Strategic Seaports for the US DOD, and its airport is among the top five in the world for cargo
throughput.
However, maritime security is a national concern and although our focus is on the Arctic environment, we
will expand our scope to include other areas in the Lower 48 states. In particular, we will develop sensor
systems, decision support tools, ice and oil spill models that include oil in ice, and educational programs
that are applicable to the Arctic as well as to the Great Lakes and Northeast.
The planned work as detailed in this document addresses the DHS mission as detailed in the National
Strategy for Maritime Security, in particular, the mission to Maximize Domain Awareness (pages 16 and
17.) This COE will produce systems to aid in accomplishing two of the objectives of this mission. They
are: 1) Sensor Technology developing sensor packages for airborne, underwater, shore-based, and
offshore platforms, and 2) Automated fusion and real-time simulation and modeling systems for decision
support and planning. An integral part of our efforts will be to develop new methods for sharing of data
between platforms, sensors, people, and communities.United States Department of Homeland SecurityCOE ADAC Objective/Purpose / Methodology / Center Management Team and Partners / Evaluation and Transition Plans / USCG Stakeholder Engagement / Workforce Development Strategy / Individual Work Plan by Projects Within a Theme / Appendix A / Appendix B / Appendix
Building Blocks for IoT Analytics Internet-of-Things Analytics
Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)
Materials science on parabolic aircraft: The FY 1987-1989 KC-135 microgravity test program
This document covers research results from the KC-135 Materials Science Program managed by MSFC for the period FY87 through FY89. It follows the previous NASA Technical Memorandum for FY84-86 published in August 1988. This volume contains over 30 reports grouped into eight subject areas covering acceleration levels, space flight hardware, transport and interfacial studies, thermodynamics, containerless processing, welding, melt/crucible interactions, and directional solidification. The KC-135 materials science experiments during FY87-89 accomplished direct science, preparation for space flight experiments, and justification for new experiments in orbit
Code-Switched Urdu ASR for Noisy Telephonic Environment using Data Centric Approach with Hybrid HMM and CNN-TDNN
Call Centers have huge amount of audio data which can be used for achieving
valuable business insights and transcription of phone calls is manually tedious
task. An effective Automated Speech Recognition system can accurately
transcribe these calls for easy search through call history for specific
context and content allowing automatic call monitoring, improving QoS through
keyword search and sentiment analysis. ASR for Call Center requires more
robustness as telephonic environment are generally noisy. Moreover, there are
many low-resourced languages that are on verge of extinction which can be
preserved with help of Automatic Speech Recognition Technology. Urdu is the
most widely spoken language in the world, with 231,295,440 worldwide
still remains a resource constrained language in ASR. Regional call-center
conversations operate in local language, with a mix of English numbers and
technical terms generally causing a "code-switching" problem. Hence, this paper
describes an implementation framework of a resource efficient Automatic Speech
Recognition/ Speech to Text System in a noisy call-center environment using
Chain Hybrid HMM and CNN-TDNN for Code-Switched Urdu Language. Using Hybrid
HMM-DNN approach allowed us to utilize the advantages of Neural Network with
less labelled data. Adding CNN with TDNN has shown to work better in noisy
environment due to CNN's additional frequency dimension which captures extra
information from noisy speech, thus improving accuracy. We collected data from
various open sources and labelled some of the unlabelled data after analysing
its general context and content from Urdu language as well as from commonly
used words from other languages, primarily English and were able to achieve WER
of 5.2% with noisy as well as clean environment in isolated words or numbers as
well as in continuous spontaneous speech.Comment: 32 pages, 19 figures, 2 tables, preprin
- …