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The list of tables of contents (TOCs) are now available for May 2019. The TOCs are formatted to the style of its publication and offers not only links to the full issue on the front cover, but also links to the individual articles within each TOC to take you to IEEEXplore®.
We are looking for a research engineer in the area of Machine Learning and Deep Neural Networks. You will join our core research team in the Automotive department and help us push the state of the art in Natural Language Processing and related fields, such as Question Answering and Semantic Parsing. You will contribute to the next generation of our in-car voice user interface and virtual assistant.
This position is located in our office in Aachen, Germany.
We are looking for an excellent Senior Research Scientist in the area of Machine Learning and Deep Neural Networks. You will join our core research team in the Automotive department and help us push the state of the art in Natural Language Processing and related fields, such as Emotion Recognition, Question Answering, and Semantic Parsing. You will contribute to the next generation of our in-car voice user interface and virtual assistant.
You will contribute to the next generations of automatic speech recognition (ASR) for automotive applications on embedded platforms and on the cloud. As senior research engineer you will investigate and implement speech recognition algorithms focusing on the integration of the newest deep learning based techniques targetting continuously listening applications.
As a Research Application Scientist you will support us in the R&D in natural language understanding (NLU) for Japanese. You will assist in all aspects of planning, developing and maintaining high-quality NLU systems for customers. The job includes new feature development, accuracy improvement, project maintenance, and language-targeted research for improvements.
The position can be filled in the following office locations: Aachen (Germany) or in Burlington, MA (US)
As a Principal Research Scientist you will do research into all aspects of speech recognition algorithms with the aim of optimizing accuracy. This will include research into algorithms to increase robustness to various acoustic conditions encountered in real-world data, as well as underlying modelling techniques.
This position can be filled in our office in Cambridge, UK.
Responsibilities:
The candidate is expected to contribute to cutting edge research in Artificial Intelligence and Machine Learning applications.
Each year, the IEEE Board of Directors confers the grade of Fellow on up to one-tenth of one percent of the voting members. To qualify for consideration, an individual must have been a Member, normally for five years or more, and a Senior Member at the time for nomination to Fellow.
It is well known that the convergence of Gaussian belief propagation (BP) is not guaranteed in loopy graphs. The classical convergence conditions, including diagonal dominance, walk-summability, and convex decomposition, are derived under pairwise factorizations of the joint Gaussian distribution. However, many applications run Gaussian BP under high-order factorizations, making the classical results not applicable.
Spike estimation from calcium (Ca
This paper introduces a node-asynchronous communication protocol in which an agent in a network wakes up randomly and independently, collects states of its neighbors, updates its own state, and then broadcasts back to its neighbors. This protocol differs from consensus algorithms and it allows distributed computation of an arbitrary eigenvector of the network, in which communication between agents is allowed to be directed.
In this paper, we study the problem of recovering a group sparse vector from a small number of linear measurements. In the past, the common approach has been to use various “group sparsity-inducing” norms such as the Group LASSO norm for this purpose. By using the theory of convex relaxations, we show that it is also possible to use 1 -norm minimization for group sparse recovery.
Lecture Date: June 2, 2019
Chapter: Toronto
Chapter Chair: Mehnaz Shokrollahi
Topic: Hyperspectral Unmixing: Insights and Beyond
Lecture Date: May 31, 2019
Chapter: Oregon
Chapter Chair: Jinsub Kim
Topic: Hyperspectral Unmixing: Insights and Beyond
IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC. Visit the SPS Job Submission Form.
Due to overwhelming interest in Member Driven Initiative funding, the IEEE Signal Processing Society has changed its process for submitting proposals through the program. This includes proposals for Forums, Regional Meetings, and Chapter Driven Initiatives.
Signal processing has played a foundational role in the consumer electronics revolution of the past several decades. Mobile devices, smart-home technologies, digital cameras, and countless other cutting-edge products have benefited from signal processing-enabled innovations.
The widespread adoption of new 3D sensor and authoring technologies has made it possible to capture 3D scenes and models in real time with decent visual quality. As an example, Microsoft's Kinect and Apple's PrimeSense technology are now being used in a wide variety of interactive 3D mobile applications, including gaming and augmented reality applications.
In today's era of the Internet of Things (IoT), the amalgamation of information and communication technologies with actuating devices has reached all corners of the modern world. In the context of critical infrastructures, such as the power grid, this cyberphysical transformation has permeated all system levels as evident in devices ranging from crucial operational components (e.g., generators) and advanced sensors...
The Multimedia Signal Processing Technical Committee (MMSP TC) of the IEEE Signal Processing Society (SPS) promotes the advancement of multimedia signal processing technology.
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