iTa-DFiE: An Innovative Tensor Algebra-Based Detection Framework for Incomplete Noninvasive Electroencephalography
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2024.3393413
Abstract: The paper presents a novel recognition framework for incomplete noninvasive Electroencephalography (EEG) signals relying on the recent advances in tensor algebra, named as An Innovative Tensor Algebra-based Detection Framework for Incomplete Noninvasive Electroencephalography (iTa-DFiE). iTa-DFiE… read more here.
Keywords: tensor; framework; underline underline; algebra ... See more keywords
On the Design of a Long Range WSN for Precision Irrigation
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Sensors Journal"
DOI: 10.1109/jsen.2017.2776859
Abstract: This research presents a WSN computer system model applied to precision irrigation, specifically for conditions in which sensor nodes may be located far from the coordinator. The proposed model is based on a tailored hardware… read more here.
Keywords: system; underline underline; model; sensor nodes ... See more keywords
STONNE: Enabling Cycle-Level Microarchitectural Simulation for DNN Inference Accelerators
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Computer Architecture Letters"
DOI: 10.1109/lca.2021.3097253
Abstract: The design of specialized architectures for accelerating the inference procedure of Deep Neural Networks (DNNs) is a booming area of research nowadays. While first-generation rigid accelerator proposals used simple fixed dataflows tailored for dense DNNs,… read more here.
Keywords: level microarchitectural; dnn; underline underline; cycle level ... See more keywords
UAVM: Towards Unifying Audio and Visual Models
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2022.3224688
Abstract: Conventional audio-visual models have independent audio and video branches. In this work, we unify the audio and visual branches by designing a Unified Audio-Visual Model (UAVM). The UAVM achieves a new state-of-the-art audio-visual event classification… read more here.
Keywords: underline underline; audio visual; towards unifying; visual models ... See more keywords
Linguistic Steganalysis via Text Dual Attention Fusing Statistical and Multi-Layer Semantic Features
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2025.3538494
Abstract: Linguistic steganalysis faces the challenge of increasingly high-quality stego text, making it difficult to distinguish these text from cover text. The two main issues with current methods are: 1) deep learning models tend to overfit,… read more here.
Keywords: layer; layer semantic; linguistic steganalysis; underline underline ... See more keywords
HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Computers"
DOI: 10.1109/tc.2024.3449108
Abstract: Graph Neural Networks (GNNs) are becoming increasingly popular for graph-based learning tasks such as point cloud processing due to their state-of-the-art (SOTA) performance. Nevertheless, the research community has primarily focused on improving model expressiveness, lacking… read more here.
Keywords: memory; edge devices; underline underline; mml mml ... See more keywords
A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation
Sign Up to like & getrecommendations! Published in 2021 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2019.2963873
Abstract: Traditional clustering algorithms for medical image segmentation can only achieve satisfactory clustering performance under relatively ideal conditions, in which there is adequate data from the same distribution, and the data is rarely disturbed by noise… read more here.
Keywords: underline underline; transfer resistant; model; transfer ... See more keywords
D²-MLoc: Dual-Domain MLP-Mixer Framework for CSI-Fingerprinting Indoor Localization
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Cognitive Communications and Networking"
DOI: 10.1109/tccn.2025.3530698
Abstract: Recent advancements in wireless communication, notably the shift towards 5G and the anticipated evolution to 6G, have significantly increased the importance of channel state information (CSI) in achieving high-accuracy indoor localization. These advancements promise better… read more here.
Keywords: mloc; indoor localization; underline underline; mixer ... See more keywords
Efficient and Secure Federated Learning Against Backdoor Attacks
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2024.3354736
Abstract: Due to the powerful representation ability and superior performance of Deep Neural Networks (DNN), Federated Learning (FL) based on DNN has attracted much attention from both academic and industrial fields. However, its transmitted plaintext data… read more here.
Keywords: backdoor; backdoor attacks; underline underline; secure ... See more keywords
RAFLS: RDP-Based Adaptive Federated Learning With Shuffle Model
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2024.3429503
Abstract: Federated Learning (FL) realizes distributed machine learning training via sharing model updates rather than raw data, thus ensuring data privacy. However, an attacker may infer the client's local original data from the model parameter so… read more here.
Keywords: underline underline; privacy; rafls; mml mml ... See more keywords
Comments on “Near-Field Prediction in Complex Environment Based on Phaseless Scanned Fields and Machine Learning”
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Electromagnetic Compatibility"
DOI: 10.1109/temc.2025.3605103
Abstract: In [1], a machine learning (ML) method for the estimation of near-field emissions is applied. The method trains and validates a four-layer artificial neural network (ANN) using transverse magnetic field amplitude data, $|H_{x}(\underline{r}_{i})|$ and $|H_{y}(\underline{r}_{i})|$,… read more here.
Keywords: field; underline underline; machine learning; lbrace underline ... See more keywords