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Section: Publications & Resources
This paper considers the problem of decentralized consensus optimization over a network, where each node holds a strongly convex and twice-differentiable local objective function. Our goal is to minimize the sum of the local objective functions and find the exact optimal solution using only local computation and neighboring communication.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Spatial registration and track-to-track association (which are mutually coupled) are essential parts in the process of multi-sensor information fusion. The quality of the spatial registration and track association algorithm directly influences the subsequent fusion performance.
Section: Publications & Resources
Publication: IEEE Open Journal of Signal Processing
Target source extractionis significant for improving human speech intelligibility and the speech recognition performance of computers. This study describes a method for target source extraction, called the similarity-and-independence-awarebeamformer (SIBF). The SIBF extracts the target source using a rough magnitude spectrogram as the reference signal. The advantage of the SIBF is that it can obtain a more accurate signal than the spectrogram generated by target-enhancing methods such as speech enhancement based on deep neural networks.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
This paper addresses the problem of target detection against a background of Gaussian clutter by using frequency snapshots with reduced degrees of freedom (DOF). We derive the optimal detector and detection performance under the Neyman-Pearson criterion for general frequency snapshot selection with arbitrary DOF. When the clutter statistics are unknown, we use a uniformly random frequency snapshot selection method and show how the DOF employed affects the detection performance.
Section: Publications & Resources
The Nominations and Appointments Subcommittee for each Technical Committee is currently seeking nominations for new Members, as well as the Vice Chair position for some Technical Committees. Nominations for both positions should be submitted directly to each Technical Committee’s Nominations and Appointments Subcommittee. Please provide the name, contact information and biography with the nomination.
Section: Publications & Resources
In an effort to continue our programs and services during this difficult time, the SPS Awards Board is now accepting nominations for all Society level awards, from paper awards to the major society awards. Nominations are due by 1 September 2021.
Section: Publications & Resources
On 14 May 2021, G. Renu opened the inaugural 2021 Regional Meeting on Deep Learning and Biomedical Signals with a welcome address and the opening curtain and lamp lighting ceremony was conducted by Mr. Deepak Mathur, Director, IEEE Region 10.
Section: Publications & Resources
Tomas Mikolov is the creator of Word2Vec which was developed in 2013 by a group of researchers led by him at Google Brain and has been widely used since then (28,000 citations). He is a pioneer for leveraging Recurrent Neural Networks (RNNs) for natural language which replaced n-gram language models that were state of the art for decades.
Section: Publications & Resources
Publication: IEEE Journal of Selected Topics in Signal Processing
Edge networks offer a promising solution for satisfying the increasing energy and computation needs of user devices with new data intensive services. A mutil-access edge computing (MEC) system with collocated MEC servers and base-stations/access points (BS/AP) has the ability to support multiple users for both data computation and wireless charging. We propose an integrated solution for wireless charging with computation offloading to satisfy the largest feasible proportion of requested wireless charging while keeping the total energy consumption at the minimum, subject to the MEC-AP transmit power and latency constraints.
Section: Publications & Resources
Publication: IEEE Open Journal of Signal Processing
In the era of big data, profitable opportunities are becoming available for many applications. As the amount of data keeps increasing, machine learning becomes an attractive tool to analyze the information acquired. However, harnessing meaningful data remains a challenge. The machine learning tools employed in many applications apply all training data without taking into consideration how relevant are some of them. In this paper, we propose a data selection strategy for the training step of Neural Networks to obtain the most significant data information and improve algorithm performance during training.
