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

爱范儿
爱范儿
博客园 - 【当耐特】
量子位
Engineering at Meta
Engineering at Meta
博客园_首页
大猫的无限游戏
大猫的无限游戏
IT之家
IT之家
V
Visual Studio Blog
小众软件
小众软件
阮一峰的网络日志
阮一峰的网络日志
美团技术团队
Jina AI
Jina AI
The Cloudflare Blog
T
Tailwind CSS Blog
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
雷峰网
雷峰网
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
宝玉的分享
宝玉的分享
WordPress大学
WordPress大学
有赞技术团队
有赞技术团队
酷 壳 – CoolShell
酷 壳 – CoolShell
Last Week in AI
Last Week in AI
人人都是产品经理
人人都是产品经理
博客园 - 聂微东

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
Orthogonal Linear Array based Product Beamforming for Rea...
Mimisha M Menakath, Mahesh Raveendranatha Panicker, Hareesh G · 2024-11-14 · via eess.SP updates on arXiv.org

Ocean exploration using acoustical 3D imaging is gaining popularity as it provides information about the 3D geometry of the targets even under mild turbid conditions. A major challenge in underwater 3D imaging is the high cost of the planar arrays and the computational complexity of the image reconstruction algorithms. In this work, we introduce two novel aspects, an L shaped array and a quadrant based time domain receive beamforming with a focus on achieving relatively low computational complexity for real-time 3D imaging in underwater environments. An orthogonal combination of two linear arrays to form an L shape is used to perform two independent and parallel 2D delay and sum beamforming and the 3D image of the target is reconstructed using the product of the resulting beams. In the proposed quadrant-based beamforming, each quadrant in the imaging slice is reconstructed in parallel using the orthogonal L-shaped linear arrays which reside at the edges of the planar array for the quadrant to be reconstructed. The proposed L-array solves the multiple-target ambiguity issue of the cross-array and an L-array placed at the center of the uniform planar array. The proposed method has reduced the main lobe width by 1 degree with 11.8 dB increase in the peak side lobe level when compared to the conventional delay and sum beamforming using a uniform planar array. Although there is an increase in the side lobe level, the asymmetric beam pattern of the proposed L array and the quadrant-based beamforming restrict the side lobes within a quadrant. The proposed method achieves a reduction in computation time by a factor of 97 for 3D imaging compared to the conventional method, while maintaining acceptable image quality. For qualitative analysis, the 3D images of different underwater targets have been reconstructed and compared in simulation and experiment.