Search Results for "joint"

A design paradigm and corresponding enabling technologies, in which sensing and comms systems are integrated to efficiently utilize congested wireless/hardware resources, and even to pursue mutual benefits. Waveform Design and Signal Processing Aspects for Fusion of Wireless Communications and Radar Sensing. Supported by IEEE ComSoc ISAC Emerging Technology Initiative (ETI). Integrating Sensing and Communications for Ubiquitous IoT. Include reproducible codes, good papers and a research libary.

MTCNN_face_detection_alignment is an implementation of the “Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks” algorithm. The algorithm uses a cascade of three convolutional networks (P-Net, R-Net, O-Net) to jointly detect faces (bounding boxes) and align facial landmarks in a coarse-to-fine manner, leveraging multi-task learning. Non-maximum suppression and bounding box regression at each stage.

The “NTU RGB+D” repository provides access to a large-scale dataset for human action recognition (and its extension, NTU RGB+D 120). The dataset includes multiple modalities (RGB video, depth sequences, infrared video, 3D skeletal joint data) captured with multiple Kinect v2 cameras simultaneously. The repository also contains MATLAB / Python demo scripts for loading, visualizing, and processing skeleton data, mapping between modalities, and handling dataset structure. Multi-modal action recognition dataset, RGB, depth, infrared, skeletal data. Split into background / evaluation sets for one-shot evaluation (in the extended dataset).

CRF-RNN is a deep neural architecture that integrates fully connected Conditional Random Fields (CRFs) with Convolutional Neural Networks (CNNs) by reformulating mean-field CRF inference as a Recurrent Neural Network. This fusion enables end-to-end training via backpropagation for semantic image segmentation tasks, eliminating the need for separate, offline post-processing steps. Our work allows computers to recognize objects in images, what is distinctive about our work is that we also...

MatODE is a simple Matlab toolbox for interfacing to the Open Dynamics Engine rigid body physics simulator (ODE, by Russell Smith), created by the Delft Biorobotics Lab (Delft University of Technology, Delft, The Netherlands). The system to be simulated is defined in an XML file, while the joint positions and motor signals can be sensed and actuated from a Matlab script. Examples are provided. Starting at version 0.30, MatODE is only available for Windows and Linux 64-bit. For older 32-bit versions, select "Browse all files".

RIPE (Regulatory network Inference from joint Perturbation and Expression data) is a novel three-step method that integrates both perturbation data and steady state gene expression data in order to estimate a regulatory network. The ripe package is written in R, with additional functionality provided by a MATLAB executable file. The executable file uses a runtime engine called the MATLAB Compiler Runtime (MCR).

This project presents a statistical model of a jointly optimized beamformer-assisted acoustic echo canceler (AEC). The constrained joint optimization problem can be formulated so that it becomes equivalent to the linearly-constrained minimum variance problem. The new formulation leads to analytical models that can be used to predict the transient performance of adaptive wideband beamformers. A stochastic model is derived for the transient and steady-state behaviors of the residual echo power. ...

SpecMark (spectral Markov) is a method for calculating the joint probability distribution over species in a regulatory network.