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Found 18 papers, 3 papers with code
Distributed Maximum Consensus over Noisy Links
We introduce a distributed algorithm, termed noise-robust distributed maximum consensus (RD-MC), for estimating the maximum value within a multi-agent network in the presence of noisy communication links.
Privacy-Preserving Distributed Nonnegative Matrix Factorization
To address this, we propose a privacy-preserving algorithm for fully-distributed NMF that decomposes a distributed large data matrix into left and right matrix factors while safeguarding each agent's local data privacy.
Analyzing the Impact of Partial Sharing on the Resilience of Online Federated Learning Against Model Poisoning Attacks
PSO-Fed reduces the communication load by enabling clients to exchange only a fraction of their model estimates with the server at each update round.
Real-time Multi-modal Object Detection and Tracking on Edge for Regulatory Compliance Monitoring
Regulatory compliance auditing across diverse industrial domains requires heightened quality assurance and traceability.
IoT Data Trust Evaluation via Machine Learning
These features can be used to learn ML models for recognizing the trustworthiness of IoT sensor data.
CVB: A Video Dataset of Cattle Visual Behaviors
Therefore, we introduce a new dataset, called Cattle Visual Behaviors (CVB), that consists of 502 video clips, each fifteen seconds long, captured in natural lighting conditions, and annotated with eleven visually perceptible behaviors of grazing cattle.
Point-Syn2Real: Semi-Supervised Synthetic-to-Real Cross-Domain Learning for Object Classification in 3D Point Clouds
Object classification using LiDAR 3D point cloud data is critical for modern applications such as autonomous driving.
In-situ animal behavior classification using knowledge distillation and fixed-point quantization
The evaluation results using two real-world animal behavior classification datasets show that the classification accuracy of the student GRU-MLP models improves appreciably through KD, approaching that of the teacher ResNet model.
Decentralized Optimization with Distributed Features and Non-Smooth Objective Functions
We develop a new consensus-based distributed algorithm for solving learning problems with feature partitioning and non-smooth convex objective functions.
Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data
However, the multimodal animal behavior classification algorithm based on posterior probability fusion is preferable to the one based on feature concatenation as it delivers better classification accuracy, has less computational and memory complexity, is more robust to sensor data failure, and enjoys better modularity.
Animal behavior classification via deep learning on embedded systems
We develop an end-to-end deep-neural-network-based algorithm for classifying animal behavior using accelerometry data on the embedded system of an artificial intelligence of things (AIoT) device installed in a wearable collar tag.
Animal Behavior Classification via Accelerometry Data and Recurrent Neural Networks
We study the classification of animal behavior using accelerometry data through various recurrent neural network (RNN) models.
Clustering-based Joint Channel Estimation and Signal Detection for Grant-free NOMA
Since the accuracy of the utilized clustering algorithm depends on the number of the data points available at the receiver, the proposed technique delivers a tradeoff between the accuracy and block length.
Fusing Multiple Multiband Images
We use the well-known forward observation and linear mixture models with Gaussian perturbations to formulate the maximum-likelihood estimator of the endmember abundance matrix of the fused image.
Fast and robust pushbroom hyperspectral imaging via DMD-based scanning
Using the image sensor pair, the device can simultaneously capture hyperspectral data as well as RGB images of the scene.
Hyperspectral Image Recovery via Hybrid Regularization
Numerical simulations using real hyperspectral images exhibit that the proposed algorithm offers an excellent recovery performance with a number of measurements that is only a small fraction of the hyperspectral image data size.
Recursive Total Least-Squares Algorithm Based on Inverse Power Method and Dichotomous Coordinate-Descent Iterations
We develop a recursive total least-squares (RTLS) algorithm for errors-in-variables system identification utilizing the inverse power method and the dichotomous coordinate-descent (DCD) iterations.
Analysis of a Reduced-Communication Diffusion LMS Algorithm
In diffusion-based algorithms for adaptive distributed estimation, each node of an adaptive network estimates a target parameter vector by creating an intermediate estimate and then combining the intermediate estimates available within its closed neighborhood.
