The Experts below are selected from a list of 24921 Experts worldwide ranked by ideXlab platform
Jiaming Shi - One of the best experts on this subject based on the ideXlab platform.
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discrete assembly backward traceability and semiconductor device forward traceability
2014Co-Authors: Didier Chavet, Cheeman Yu, Hem Takiar, Lu Frank, Tung Chihchiang, Jiaming ShiAbstract:The invention discloses a system for providing backward and forward traceability through a method of identifying a discrete assembly (a bare core, a substrate and/or a passive element) in a semiconductor device. A technology used for generating a Unique Identifier is further included, the Unique Identifier is used for marking the semiconductor device, and the semiconductor device and the discrete assembly in the device can be tracked and retrospected under the condition of each process and test in the process of producing the semiconductor device.
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discrete component backward traceability and semiconductor device forward traceability
2010Co-Authors: Didier Chavet, Cheeman Yu, Hem Takiar, Frank Lu, Chihchiang Tung, Jiaming ShiAbstract:A system is disclosed for providing backward and forward traceability by a methodology which identifies discrete components (die, substrate and/or passives) that are included in a semiconductor device. The present technology further includes a system for generating a Unique Identifier and marking a semiconductor device with the Unique Identifier enabling the semiconductor device, and the discrete components within that device, to be tracked and traced through each process and test in the production of the semiconductor device.
A.h. Sung - One of the best experts on this subject based on the ideXlab platform.
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HIS - Web site visitor classiflcation using machine learning
Fourth International Conference on Hybrid Intelligent Systems (HIS'04), 2004Co-Authors: P. Defibaugh-chavez, Srinivas Mukkamala, A.h. SungAbstract:Classifying Web site visitors allows organizations to present customized content and effectively allocate resources. Traditional methods of visitor classification involve tracking individual users over many sessions via a Unique Identifier such as the IP address or a cookie. These methods are either too general or strip the visitor of a level of privacy. In this paper we use machine learning techniques to classify visitors of a data-centric Web site using a minimal amount of information and without a Unique Identifier. We are able to group visitors into groups without extended user tracking.
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Web site visitor classiflcation using machine learning
Fourth International Conference on Hybrid Intelligent Systems (HIS'04), 2004Co-Authors: P. Defibaugh-chavez, Srinivas Mukkamala, A.h. SungAbstract:Classifying Web site visitors allows organizations to present customized content and effectively allocate resources. Traditional methods of visitor classification involve tracking individual users over many sessions via a Unique Identifier such as the IP address or a cookie. These methods are either too general or strip the visitor of a level of privacy. In this paper we use machine learning techniques to classify visitors of a data-centric Web site using a minimal amount of information and without a Unique Identifier. We are able to group visitors into groups without extended user tracking.
Haibin Zhang - One of the best experts on this subject based on the ideXlab platform.
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ESORICS - Unique Group Signatures
Computer Security – ESORICS 2012, 2012Co-Authors: Matthew Franklin, Haibin ZhangAbstract:We initiate the study of Unique group signature such that signatures of the same message by the same user will always have a large common component (i.e., Unique Identifier). It enables an efficient detection algorithm, revealing the identities of illegal users, which is fundamentally different from previous primitives. We present a number of Unique group signature schemes (without random oracles) under a variety of security models that extend the standard security models of ordinary group signatures. Our work is a beneficial step towards mitigating the well-known group signature paradox, and it also has many other interesting applications and efficiency implications.
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Unique Group Signatures
Computer Security – ESORICS 2012, 2012Co-Authors: Matthew Franklin, Haibin ZhangAbstract:We initiate the study of Unique group signature such that signatures of the same message by the same user will always have a large common component (i.e., Unique Identifier). It enables an efficient detection algorithm, revealing the identities of illegal users, which is fundamentally different from previous primitives. We present a number of Unique group signature schemes (without random oracles) under a variety of security models that extend the standard security models of ordinary group signatures. Our work is a beneficial step towards mitigating the well-known group signature paradox, and it also has many other interesting applications and efficiency implications.
Didier Chavet - One of the best experts on this subject based on the ideXlab platform.
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discrete assembly backward traceability and semiconductor device forward traceability
2014Co-Authors: Didier Chavet, Cheeman Yu, Hem Takiar, Lu Frank, Tung Chihchiang, Jiaming ShiAbstract:The invention discloses a system for providing backward and forward traceability through a method of identifying a discrete assembly (a bare core, a substrate and/or a passive element) in a semiconductor device. A technology used for generating a Unique Identifier is further included, the Unique Identifier is used for marking the semiconductor device, and the semiconductor device and the discrete assembly in the device can be tracked and retrospected under the condition of each process and test in the process of producing the semiconductor device.
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discrete component backward traceability and semiconductor device forward traceability
2010Co-Authors: Didier Chavet, Cheeman Yu, Hem Takiar, Frank Lu, Chihchiang Tung, Jiaming ShiAbstract:A system is disclosed for providing backward and forward traceability by a methodology which identifies discrete components (die, substrate and/or passives) that are included in a semiconductor device. The present technology further includes a system for generating a Unique Identifier and marking a semiconductor device with the Unique Identifier enabling the semiconductor device, and the discrete components within that device, to be tracked and traced through each process and test in the production of the semiconductor device.
Klaus Stark - One of the best experts on this subject based on the ideXlab platform.
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idgenerator Unique Identifier generator for epidemiologic or clinical studies
BMC Medical Research Methodology, 2016Co-Authors: Matthias Olden, Rolf Holle, Iris M Heid, Klaus StarkAbstract:Creating study Identifiers and assigning them to study participants is an important feature in epidemiologic studies, ensuring the consistency and privacy of the study data. The numbering system for Identifiers needs to be random within certain number constraints, to carry extensions coding for organizational information, or to contain multiple layers of numbers per participant to diversify data access. Available software can generate globally-Unique Identifiers, but Identifier-creating tools meeting the special needs of epidemiological studies are lacking. We have thus set out to develop a software program to generate IDs for epidemiological or clinical studies. Our software IDGenerator creates Unique Identifiers that not only carry a random Identifier for a study participant, but also support the creation of structured IDs, where organizational information is coded into the ID directly. This may include study center (for multicenter-studies), study track (for studies with diversified study programs), or study visit (baseline, follow-up, regularly repeated visits). Our software can be used to add a check digit to the ID to minimize data entry errors. It facilitates the generation of IDs in batches and the creation of layered IDs (personal data ID, study data ID, temporary ID, external data ID) to ensure a high standard of data privacy. The software is supported by a user-friendly graphic interface that enables the generation of IDs in both standard text and barcode 128B format. Our software IDGenerator can create Identifiers meeting the specific needs for epidemiologic or clinical studies to facilitate study organization and data privacy. IDGenerator is freeware under the GNU General Public License version 3; a Windows port and the source code can be downloaded at the Open Science Framework website: https://osf.io/urs2g/ .
