The Experts below are selected from a list of 2920614 Experts worldwide ranked by ideXlab platform
David Holmes - One of the best experts on this subject based on the ideXlab platform.
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Anti-NGF painkillers back on track?
Nature Reviews Drug Discovery, 2012Co-Authors: David HolmesAbstract:Following an FDA advisory committee vote to restart clinical development of nerve growth factor antagonists, could this novel class of analgesics still fulfil its once-anticipated potential?
Roedig Utz - One of the best experts on this subject based on the ideXlab platform.
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Smart Speaker privacy control - acoustic tagging for Personal Voice Assistants
2019Co-Authors: Cheng Peng, Bagci, Ibrahim Ethem, Yan Jeff, Roedig UtzAbstract:Personal Voice Assistants (PVAs) such as the Siri, Amazon Echo and Google Home are now commonplace. PVAs continuously monitor conversations which may be transported to a cloud back end where they are stored, processed and maybe even passed on to other service providers. A user has little controlover this process. She is unable to control the recording behaviour of surrounding PVAs, unable to signal her privacy requirements to back-end systems and unable to track conversation recordings.In this paper we explore techniques for embedding additional information into acoustic signals processed by PVAs. A user employs a tagging device which emits an acoustic signal whenPVA activity is assumed. Any active PVA will embed this tag into their recorded audio stream. The tag may signal a cooperatingPVA or back-end system that a user has not given a recording consent. The tag may also be used to trace when and wherea recording was taken. We discuss different tagging techniques and application scenarios, and we describe the implementation of a prototype tagging device based on PocketSphinx. Using the popular PVA Google Home Mini we demonstrate that the device can tag conversations and that the tagging signal can be retrieved from conversations stored in the Google back-end system
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Smart Speaker Privacy Control - Acoustic Tagging for Personal Voice Assistants
'Institute of Electrical and Electronics Engineers (IEEE)', 2019Co-Authors: Cheng Peng, Bagci, Ibrahim Ethem, Yan Jeff, Roedig UtzAbstract:Personal Voice Assistants (PVAs) such as the Siri, Amazon Echo and Google Home are now commonplace. PVAs continuously monitor conversations which may be transported to a cloud back end where they are stored, processed and maybe even passed on to other service providers. At present, a user has little control over this process. He is unable to control the recording behaviour of surrounding PVAs, is unable to signal his privacy requirements to back-end systems and is unable to track conversation recordings. In this paper we explore techniques for embedding additional information into acoustic signals processed by PVAs. A user employs a tagging device which emits an acoustic signal when PVA activity is assumed. Any active PVA will embed this tag in the recorded audio stream. The tag may signal a cooperating PVA or back-end system that a user has not given a recording consent. The tag may also be used to trace when and where a recording was taken. In this paper we discuss different tagging techniques and application scenarios. We describe the implementation of a prototype tagging device based on PocketSphinx. Using the popular PVA Google Home Mini we demonstrate that the device can tag conversations and that the tagging signal can be retrieved from conversations stored in the Google back-end system
Sindo Kou - One of the best experts on this subject based on the ideXlab platform.
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susceptibility of ternary aluminum alloys to cracking during solidification
Acta Materialia, 2017Co-Authors: Jiangwei Liu, Sindo KouAbstract:Abstract The crack susceptibility map of a ternary Al alloy system provides useful information about which alloy compositions are most susceptible to cracking and thus should be avoided by using a filler metal with a significantly different composition. In the present study the crack susceptibility maps of ternary Al alloy systems were calculated based on the maximum |dT/d(fS)1/2| as an index for the crack susceptibility, where T is temperature and fS fraction solid. Due to the complexity associated with ternary alloy solidification, commercial thermodynamic software Pandat and Al database PanAluminum, instead of analytical equations, were used to calculate fS as a function of T and hence the maximum |dT/d(fS)1/2| for ternary Al-Mg-Si, Al-Cu-Mg and Al-Cu-Si alloy systems. A crack susceptibility map covering 121 alloy compositions was constructed for each of the three ternary alloy systems at each of the following three levels of back diffusion: no back diffusion, back diffusion under a 100 °C/s cooling rate, and back diffusion under 20° C/s. The location of the region of high crack susceptibility, which is the most important part of the map, was shown in each of the nine calculated maps. These locations were compared with those observed in crack susceptibility tests by previous investigators. With back diffusion considered, either under 20 or 100 °C/s, the agreement between the calculated and observed maps was good especially for Al-Mg-Si and Al-Cu-Mg. Thus, the maximum |dT/d(fS)1/2| can be used as a crack susceptibility index to construct crack susceptibility maps for ternary Al alloys and to evaluate the effect of back diffusion on their crack susceptibility.
Cheng Peng - One of the best experts on this subject based on the ideXlab platform.
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Smart Speaker privacy control - acoustic tagging for Personal Voice Assistants
2019Co-Authors: Cheng Peng, Bagci, Ibrahim Ethem, Yan Jeff, Roedig UtzAbstract:Personal Voice Assistants (PVAs) such as the Siri, Amazon Echo and Google Home are now commonplace. PVAs continuously monitor conversations which may be transported to a cloud back end where they are stored, processed and maybe even passed on to other service providers. A user has little controlover this process. She is unable to control the recording behaviour of surrounding PVAs, unable to signal her privacy requirements to back-end systems and unable to track conversation recordings.In this paper we explore techniques for embedding additional information into acoustic signals processed by PVAs. A user employs a tagging device which emits an acoustic signal whenPVA activity is assumed. Any active PVA will embed this tag into their recorded audio stream. The tag may signal a cooperatingPVA or back-end system that a user has not given a recording consent. The tag may also be used to trace when and wherea recording was taken. We discuss different tagging techniques and application scenarios, and we describe the implementation of a prototype tagging device based on PocketSphinx. Using the popular PVA Google Home Mini we demonstrate that the device can tag conversations and that the tagging signal can be retrieved from conversations stored in the Google back-end system
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Smart Speaker Privacy Control - Acoustic Tagging for Personal Voice Assistants
'Institute of Electrical and Electronics Engineers (IEEE)', 2019Co-Authors: Cheng Peng, Bagci, Ibrahim Ethem, Yan Jeff, Roedig UtzAbstract:Personal Voice Assistants (PVAs) such as the Siri, Amazon Echo and Google Home are now commonplace. PVAs continuously monitor conversations which may be transported to a cloud back end where they are stored, processed and maybe even passed on to other service providers. At present, a user has little control over this process. He is unable to control the recording behaviour of surrounding PVAs, is unable to signal his privacy requirements to back-end systems and is unable to track conversation recordings. In this paper we explore techniques for embedding additional information into acoustic signals processed by PVAs. A user employs a tagging device which emits an acoustic signal when PVA activity is assumed. Any active PVA will embed this tag in the recorded audio stream. The tag may signal a cooperating PVA or back-end system that a user has not given a recording consent. The tag may also be used to trace when and where a recording was taken. In this paper we discuss different tagging techniques and application scenarios. We describe the implementation of a prototype tagging device based on PocketSphinx. Using the popular PVA Google Home Mini we demonstrate that the device can tag conversations and that the tagging signal can be retrieved from conversations stored in the Google back-end system
Y Arai - One of the best experts on this subject based on the ideXlab platform.
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ultrasonic back reflection evaluation of crack growth from psbs in low cycle fatigue of stainless steel under constant load amplitude
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2009Co-Authors: Md Nurul Islam, Y AraiAbstract:The objective of this study is to develop a method for evaluating crack growth from persistent slip bands (PSBs) in low-cycle fatigue of stainless steel, using an ultrasonic back reflection wave during the early stages of its fatigue life. Changes in the back reflection intensity from surface of the material under cyclic loading are measured. Back reflection intensity decreased due to the evolution of PSBs before the start of fatigue crack growth from the crack initiated along PSBs with increase in the number of cyclic loads. The average dislocation density in a grain including PSBs corresponds to the attenuation change measured during the fatigue test, from the initial state to the nucleation and growth of the fatigue crack. The attenuation is caused by the movement of dislocation due to ultrasonic waves, whose mechanism was considered quantitatively. In this study, micromechanical modeling was conducted as a prediction method for remaining fatigue life to start crack growth from PSBs based on the changes in ultrasonic back reflection intensity.
