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The Latest News, Articles, and Events in Signal Processing
A Chapter is required to report and file not less than two (2) technical meetings per year. Changes to its roster of officers are to be submitted in a timely manner using electronic reporting tools provided by MGA.
Attn: Section Chairs, Section Student Activities Chairs, and Section Student Representatives
Below are the actions that have been approved by the MGA Board in relation to the Section - Student Branch Connection.
The IEEE Signal Processing Society Malaysia Chapter has been selected as the recipient of the 2018 Chapter of the Year Award!
Please be advised that IEEE gauges the vitality of an OU by tracking how many meetings are reported during the year through vTools. If an OU reports "0" meetings for three consecutive years the OU/Chapter is placed on a dissolution list that is reviewed at the November Board Meeting.
A new online portal is available for ordering Membership Development (MD) materials. Volunteers now have the ability to customize the quantities and items included to support in recruiting new members, retaining those whose membership has lapsed, and recovering former members.
Please visit the Conferences and Events page on the IEEE Signal Processing Society website for Upcoming Lectures by Distinguished Lecturers.
The School of Informatics at the University of Edinburgh are currently recruiting faculty at the Lecturer/Senior Lecturer/Reader level (similar to Assistant/Associate Professor) in several areas, including speech technology.
In this paper, four iterative algorithms for learning analysis operators are presented. They are built upon the same optimization principle underlying both Analysis K-SVD and Analysis SimCO. The forward and sequential analysis operator learning (AOL) algorithms are based on projected gradient descent with optimally chosen step size. The implicit AOL algorithm is inspired by the implicit Euler scheme for solving ordinary differential equations and does not require to choose a step size.
We generalize the 1-bit matrix completion problem to higher order tensors. Consider a rank-
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes.
Extracting information from a signal exhibiting damped resonances is a challenging task in many practical cases due to the presence of noise and high attenuation. The interpretation of the signal relies on a model whose order (i.e., the number of resonances) is in general unknown.
This study investigates various aspects of multi-speaker interference and its impact on speaker recognition. Single-channel multi-speaker speech signals (aka co-channel speech) comprise a significant portion of speech processing data. Examples of co-channel signals are recordings from multiple speakers in meetings, conversations, debates, etc.
Improving the modeling and processing of nonstationary signals remains an important yet challenging problem. In the past, the most effective approach for processing these signals has been statistical modeling.
Machine learning and related statistical signal processing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things (IoT). As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by system architects and design engineers, and present a new set of design trade-offs.
The Bio-Imaging and Signal Processing Technical Committee (BISP-TC) of the IEEE Signal Processing Society (SPS) promotes activities in the broad technical areas of computerized image and signal processing with a clear focus on applications in biology and medicine.
As part of the IEEE Signal Processing Society (SPS), the Speech and Language Technical Committee (SLTC) promotes research and development activities for technologies that are used to process speech and natural language.
5G technology, with its promises of self-driving vehicles and immersive virtual reality, will be a data-hungry generation of wireless communications.
In the era of big data, analysts usually explore various statistical models or machine-learning methods for observed data to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates.
This month's special issue of Proceedings of the IEEE provides a state-of-the-art overview of the field of silicon photonics, which is making a significant impact on fiber-optic communications and spreading to new areas such as sensors and deep learning.
In the era of big data, analysts usually explore various statistical models or machine-learning methods for observed data to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates.
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- THIS WEEK: Join NASA’s Dr. Jacqueline Le Moigne as she shares her journey through academia, the private sector, and pivotal roles at NASA, emphasizing her work in signal processing, computer vision, and related technologies. Register now! https://x.com/IEEEsps/status/1785057479606288505
- Join NASA’s Dr. Jacqueline Le Moigne for this interactive webinar as she shares her journey through the realms of signal processing, computer vision, and related technologies, including her pivotal roles at NASA. https://x.com/IEEEsps/status/1782468413551423536
- Great crowd at the Student Job Fair at #ICASSP2024! Thank you to our sponsors for furnishing an exciting, engaging event! https://x.com/IEEEsps/status/1780817453569687559
- Thank you to our Women in Signal Processing Luncheon panelists for their wisdom and insights during today’s event at #ICASSP2024! https://x.com/IEEEsps/status/1780458637338530252
- Free Machine Learning (ML) Lecture Series from IEEE SPS From basics to recent advances, unlock the secrets of ML with Prof. Sergios Theodoridis! https://x.com/IEEEsps/status/1779931297093222415
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