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Issue 4 • April-2025
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Advanced Spectroscopy Time-Domain Signal Simulator for the Development of Machine and Deep Learning Algorithms
Publication Year: 2025,Article Sequence Number: 3501004
Machinelearning methods, particularly deep learning (DL), have become essential for advanced signal processing. These methods often depend on annotated datasets, which can be limited or even unavailable in many cases. One area significantly affected is nuclear spectroscopy, where the lack of annotated datasets is due to the challenges of manually labeling signals recorded in the time domain. To ad...Show More
Advanced Spectroscopy Time-Domain Signal Simulator for the Development of Machine and Deep Learning Algorithms
Year: 2025 | Volume: 9 | Issue: 4
M2S2: A Multimodal Sensor System for Remote Animal Motion Capture in the Wild
Azraa Vally;Gerald Maswoswere;Nicholas Bowden;Stephen Paine;Paul Amayo;Andrew Markham;Amir Patel
Publication Year: 2025,Article Sequence Number: 5501104
Capturing animal locomotion in the wild is far more challenging than in controlled laboratory settings. Wildlife subjects move unpredictably, and issues, such as scaling, occlusion, lighting changes, and the lack of ground truth data, make motion capture difficult. Unlike human biomechanics, where machine learning thrives with annotated datasets, such resources are scarce for wildlife. Multimodal ...Show More
Smoothing Objective Function for 3-D Electrical Resistivity Inversion by CNNs Regularizer
Peng Jiang;Shengjie Qiao;Yonghao Pang;Yongheng Zhang;Zhengyu Liu
Publication Year: 2025,Article Sequence Number: 7001504
In tunnel geological forecasting, the electrical resistivity inversion method is extensively employed due to its high sensitivity to water-bearing bodies. Traditional inversion methods, such as least squares, simplify nonlinear problems into linear ones. However, they often converge to local minima, making it challenging to identify the global optimal solution, and their inversion results are high...Show More
No-Reference Laparoscopic Video Quality Assessment for Sensor Distortions Using Optimized Long Short-Term Memory Framework
Publication Year: 2025,Article Sequence Number: 7001604
Laparoscopic surgery relies on sensor-based video systems vulnerable to visual distortions, requiring rigorous quality checks to meet regulatory standards. This letter introduces a no-reference laparoscopic video quality assessment algorithm designed to replicate human perceptual judgments in the presence of sensor distortions. The method models the statistical interdependencies between luminance ...Show More
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Editor-in-Chief
Andrei Shkel
University of California - Irvine, Samueli School of Engineering
Irvine
USA
andrei.shkel@uci.eduContact Information
Editor-in-Chief
Andrei Shkel
University of California - Irvine, Samueli School of Engineering
Irvine
USA
andrei.shkel@uci.eduIEEE Account
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