A Deep Learning Framework of Quantized Compressed Sensing for Wireless Neural Recording
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Summary
A deep learning framework of quantized CS, termed BW-NQ-DNN, is proposed, which consists of a binary measurement matrix, a non-uniform quantizer, and aNon-iterative recovery solver that achieves high recovery performance and spike classification accuracy on the challenging high compression ratio task.
- Type
- article
- Published
- 2016-09-05
- Cited by
- 51
- References
- 48
- Access
- Open access
- OpenAlex
- https://openalex.org/W2507344106
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:38298149
Keywords
Computer science, Compressed sensing, Wireless, Artificial neural network, Transmission (telecommunications)
References
- Feature Selection Using a Multilayer Perceptron
- Compressed Sensing
- An unsupervised dictionary learning algorithm for neural recordings
- Foundations of Quantization for Probability Distributions
- Deterministic Construction of Sparse Sensing Matrices via Finite Geometry
- Message-Passing De-Quantization With Applications to Compressed Sensing
- Intracellular features predicted by extracellular recordings in the hippocampus in vivo.
- Wireless neurosensor for full-spectrum electrophysiology recordings during free behavior.
- Power-efficient VLSI implementation of a feature extraction engine for spike sorting in neural recording and signal processing
- A Pragmatic Look at Some Compressive Sensing Architectures With Saturation and Quantization
- Atomic Decomposition by Basis Pursuit
- Spectra of quantized signals
- Splines: a perfect fit for signal and image processing
- Energy-Efficient Multi-Mode Compressed Sensing System for Implantable Neural Recordings
- Chronic, Wireless Recordings of Large Scale Brain Activity in Freely Moving Rhesus Monkeys
- Asymptotic analysis of optimal fixed-rate uniform scalar quantization
- An Efficient and Compact Compressed Sensing Microsystem for Implantable Neural Recordings
- Adaptive compressed sensing architecture in wireless brain-computer interface
- Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors
- Message-passing algorithms for compressed sensing
Cited by
- A Training-Free One-Bit Compressed Sensing Framework for Wireless Neural Recording
- SVM and ANFIS as channel selection models for the spectrum decision stage in cognitive radio networks
- Deep learning sparse ternary projections for compressed sensing of images
- A sparse autoencoder compressed sensing method for acquiring the pressure array information of clothing
- Primary user characterization for cognitive radio wireless networks using a neural system based on Deep Learning
- Semi-Supervised Deep Blind Compressed Sensing for Analysis and Reconstruction of Biomedical Signals From Compressive Measurements
- A new multilayer LSTM method of reconstruction for compressed sensing in acquiring human pressure data
- Distributed Compressed Sensing of Microseismic Signals Through First Break Time Extraction and Signal Alignment
- Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey
- Study of Compressed Sensing and Predictor Techniques for the Compression of Neural Signals under the Influence of Noise
- Primary user characterization for cognitive radio wireless networks using long short-term memory
- A Review of Sparse Recovery Algorithms
- Wavelet Denoising Algorithm Based on NDOA Compressed Sensing for Fluorescence Image of Microarray
- A Learning-Based Framework for Quantized Compressed Sensing
- Compressed Remote Sensing by Using Deep Learning
- Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis
- Joint Design of Measurement Matrix and Sparse Support Recovery Method via Deep Auto-Encoder
- DeepQuantizedCS: Quantized Compressive Video Recovery using Deep Convolutional Networks
- Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals
- Dynamic Centripetal Parameterization Method for B-Spline Curve Interpolation
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