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Found 15 papers, 12 papers with code
Diffusion-Aided Joint Source Channel Coding For High Realism Wireless Image Transmission
In the following diffusion step, DiffJSCC uses the derived multimodal features, together with channel state information such as the signal-to-noise ratio (SNR), as conditions to guide the denoising diffusion process, which converts the initial random noise to the final reconstruction.
Differentiable Learning of Generalized Structured Matrices for Efficient Deep Neural Networks
To address this issue, we propose a generalized and differentiable framework to learn efficient structures of weight matrices by gradient descent.
Siamese Learning-based Monarch Butterfly Localization
A new GPS-less, daily localization method is proposed with deep learning sensor fusion that uses daylight intensity and temperature sensor data for Monarch butterfly tracking.
MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding
In this paper, we propose multi-mode video compression (MMVC), a block wise mode ensemble deep video compression framework that selects the optimal mode for feature domain prediction adapting to different motion patterns.
Efficient Computation Sharing for Multi-Task Visual Scene Understanding
Solving multiple visual tasks using individual models can be resource-intensive, while multi-task learning can conserve resources by sharing knowledge across different tasks.
Deep Joint Source-Channel Coding with Iterative Source Error Correction
In this paper, we propose an iterative source error correction (ISEC) decoding scheme for deep-learning-based joint source-channel coding (Deep JSCC).
Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection
In recent years, deep learning-based approaches for visual-inertial odometry (VIO) have shown remarkable performance outperforming traditional geometric methods.
Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring
Millimeter-scale embedded sensing systems have unique advantages over larger devices as they are able to capture, analyze, store, and transmit data at the source while being unobtrusive and covert.
Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control
To the best of our knowledge, this is the first deep JSCC scheme that can automatically adjust its rate using a single network model.
Unified Signal Compression Using a GAN with Iterative Latent Representation Optimization
We propose a unified signal compression framework that uses a generative adversarial network (GAN) to compress heterogeneous signals.
OFDM-guided Deep Joint Source Channel Coding for Wireless Multipath Fading Channels
We investigate joint source channel coding (JSCC) for wireless image transmission over multipath fading channels.
Deep Learning in Latent Space for Video Prediction and Compression
The proposed method first learns the efficient lower-dimensional latent space representation of each video frame and then performs inter-frame prediction in that latent domain.
Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM
We present a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping.
Migrating Monarch Butterfly Localization Using Multi-Sensor Fusion Neural Networks
Details of Monarch butterfly migration from the U. S. to Mexico remain a mystery due to lack of a proper localization technology to accurately localize and track butterfly migration.
Unified Signal Compression Using Generative Adversarial Networks
We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals.
