1 citations · 1 across the 15 of their papers we have counts for
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FedLoRD: Personalized Federated Learning based on Low-Rank Structures of Model Parameter Differences
Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi +1
We propose FedLoRD, a personalized federated learning (PFL) method based on the low-rank structure of the differences between the global and personalized model parameters. PFL is a…
Ternary Visible Light Communication Using Event-Based Vision Sensors
Sotaro Kuremoto, Junya Hara, Hiroshi Higashi +1
This paper proposes a ternary visible light communication method using an event-based vision sensor (EVS) and a liquid crystal display (LCD). The throughput of optical camera commu…
Multivariate signal restoration via Multifold Graph Learning
Haruki Yokota, Yuichi Tanaka
We propose a method for restoring noisy and incomplete multivariate signals by learning multifold graphs. Spatial relationships among channels can be utilized to recover missing va…
TDOA-Based Online Target Tracking with Simultaneous Sensor Pairing and Relocation
Ryosuke Ikura, Junya Hara, Hiroshi Higashi +1
We propose a target tracking method for mobile sensor networks (MSNs) based on time difference-of-arrival (TDOA). Target tracking using an MSN requires reorganizing sensor pairs an…
WREN: Low Light Image Enhancement Using Retinex theory-based Double U-Net-like Structures
Reina Kaneko, Junya Hara, Hiroshi Higashi +1
This paper proposes a neural network for low light image enhancement (LLIE) based on retinex theory to make LLIE robust for various dynamic range scenes. The retinex theory is an i…
Lossy compression of weighted graph adjacency matrices by transform coding
Kenta Yanagiya, Junya Hara, Hiroshi Higashi +2
In this paper, we propose a compression framework for weighted graphs in which the graph topology is transmitted losslessly and edge weights are compressed lossily. A challenge in…
Multimodal Signal Restoration with Signed Twofold Graph Learning
Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka
We propose a method for jointly learning signed twofold graphs and performing signal restoration on multimodal graph signals. Multimodal signals on sensor networks are commonly mod…
Joint Alignment and Denoising for Event-Based Vision Sensors Using Regret-based Pareto Optimization
Shimpei Harada, Junya Hara, Hiroshi Higashi +1
This paper proposes a joint alignment and denoising method for event-based vision sensors (EVSs). Existing signal processing methods for EVSs typically perform event alignment (EA)…
Denoising for Neuromorphic Cameras Based on Graph Spectral Features
Shimpei Harada, Junya Hara, Hiroshi Higashi +1
Neuromorphic cameras, also known as event-based cameras, can detect changes in the environmental brightness asynchronously and independently for each pixel. They output the brightn…
Auditory Attention Decoding without Spatial Information: A Diotic EEG Study
Masahiro Yoshino, Haruki Yokota, Junya Hara +2
Auditory attention decoding (AAD) identifies the attended speech stream in multi-speaker environments by decoding brain signals such as electroencephalography (EEG). This technolog…