惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
B
Blog
Jina AI
Jina AI
N
Netflix TechBlog - Medium
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
博客园_首页
Hugging Face - Blog
Hugging Face - Blog
博客园 - 聂微东
美团技术团队
Google DeepMind News
Google DeepMind News
WordPress大学
WordPress大学
阮一峰的网络日志
阮一峰的网络日志
U
Unit 42
The Cloudflare Blog
V
V2EX
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
小众软件
小众软件
罗磊的独立博客
Microsoft Security Blog
Microsoft Security Blog
Apple Machine Learning Research
Apple Machine Learning Research
I
InfoQ
GbyAI
GbyAI
腾讯CDC
MongoDB | Blog
MongoDB | Blog

eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Frequency-Dependent F-Numbers Suppress Grating Lobes and ...
Martin F. Schiffner · 2024-10-02 · via eess.SP updates on arXiv.org

Line-by-line scanning with linear arrays is a standard image formation method in clinical ultrasound. This method examines progressively a given region of interest by conducting focused pulse-echo measurements with dynamic transmit and receive apertures. Such apertures widen with the focal length as a function of a given F-number and improve the image quality by extending the depth of field (DOF) and suppressing grating lobes. Fixed F-numbers, however, limit the lateral resolution. Herein, frequency dependence of the F-number is incorporated into both the transmit and the receive focusing to widen the apertures for low frequencies and improve the lateral resolution. Frequency-dependent transmit and receive F-numbers are proposed. These F-numbers, which can be expressed in closed form, maximize the lateral resolution under constraints on the DOF and the grating lobes. A phantom experiment showed that the proposed F-numbers eliminate grating lobe artifacts and improve both image uniformity and contrast to a similar extent as fixed F-numbers. These metrics, compared to the usage of the full apertures, improved by up to 14.1 % and 8.3 %, respectively. The proposed F-numbers, however, improved the lateral resolution by up to 24 % compared to the fixed F-numbers.