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The Latest News, Articles, and Events in Signal Processing
May 7-9, 2019
Registration Deadline: TBD
Location: Porto Alegre, Brazil
Website
Lecture Date: June 11, 2019
Chapter: Republic of Macedonia Joint
Chapter Chair: Tomislav Kartalov
Topic: Graph Signal Processing: Connections to Distributed
Optimization and Deep Learning
November 11-14, 2019
Location: Ottawa, Canada
December 9-12, 2019
Location: Delft, Netherlands
Lecture Date: April 2, 2019
Chapter: Finland
Chapter Chair: Konstantinos Drossos
Topic: Graph Signal Processing: Distributed Graph Filters
Lecture Date: April 29, 2019
Chapter: Hong Kong
Chapter Chair: Lai-Man Po
Topic: Distributed Algorithms for Principal Component Analysis
Lecture Date: April 19, 2019
Chapter: Toronto
Chapter Chair: Mehnaz Shokrollahi
Topic: Solving the Cocktail Party Problem for Hearing Aids:
Solutions, Challenges and Opportunities
Lecture Date: May 7, 2019
Chapter: Toronto
Chapter Chair: Mehnaz Shokrollahi
Topic: Cyber Attacks on Internet of Things Sensor Systems for Inference
Lecture Date: May 30, 2019
Chapter: Poland
Chapter Chair: Piotr Samczynski
Topic: Towards Autonomous Video Surveillance and
Privacy-Preserving Localization and Recognition of Human Activities
Lecture Date: May 28, 2019
Chapter: Poland
Chapter Chair: Piotr Samczynski
Topic: Towards Autonomous Video Surveillance and
Privacy-Preserving Localization and Recognition of Human Activities
Lecture Date: May 21-22, 2019
Chapter: Poland
Chapter Chair: Piotr Samczynski
Topic: Towards Autonomous Video Surveillance and
Privacy-Preserving Localization and Recognition of Human Activities
Two PhD positions in the area of Information Extraction, Data Mining and Machine Learning
at Saarland University
We anticipate the availability of funds for two PhD positions in the area of information extraction, data mining and machine learning.
Lecture Date: April 15, 2019
Chapter: Atlanta
Chapter Chair: Alessio Medda
Topic: Tackling the Cocktail Party Problem for
Hearing Devices: Solutions, Challenges and Opportunities
Radio tomographic imaging (RTI) is an emerging technology to locate physical objects in a geographical area covered by wireless networks. From the attenuation measurements collected at spatially distributed sensors, radio tomography capitalizes on spatial loss fields (SLFs) measuring the absorption of radio frequency waves at each location along the propagation path.
In this paper, we propose a regular vine copula based methodology for the fusion of correlated decisions. Regular vine copula is an extremely flexible and powerful graphical model to characterize complex dependence among multiple modalities.
This paper addresses the design and analysis of feedback-based online algorithms to control systems or networked systems based on performance objectives and engineering constraints that may evolve over time. The emerging time-varying convex optimization formalism is leveraged to model optimal operational trajectories of the systems, as well as explicit local and network-level operational constraints.
In this paper, we address the problem of observability of a linear dynamical system from compressive measurements and the knowledge of its external inputs. Observability of a high-dimensional system state in general requires a correspondingly large number of measurements.
This paper presents a comprehensive study of post-mortem human iris recognition carried out for 1200 near-infrared and 1787 visible-light samples collected from 37 deceased individuals kept in mortuary conditions. We used four independent iris recognition methods (three commercial and one academic) to analyze genuine and impostor comparison scores and check the dynamics of iris quality decay over a period of up to 814 h after death.
Video watermarking is a well-established technology to help identify digital pirates when they illegally re-distribute multimedia content. In order to provide every client with a unique, watermarked video, the traditional distribution architectures separately encode each watermarked video. However, since these encodings require a high amount of computational resources, such architectures do not scale well to a large number of users.
Despite the benefits of cyber-insurance, organizations are reluctant to enroll in such policies mainly because of their limitation and high price. On the other hand, insurers are confronting the adverse selection and moral hazard problems as monitoring and distinguishing insureds' cybersecurity posture are highly complicated. Considering the organizations' security interdependency and their demand for cyber-insurance, we study the design of coalitional insurance mechanisms...
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