27 citations · 28 across the 14 of their papers we have counts for
19 papers
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…
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…
Dynamic Sensor Pairing for TDOA-Based Target Tracking via mixed-integer Second-Order Cone Programming
Ryosuke Ikura, Junya Hara, Hiroshi Higashi +1
We propose a sensor pairing method for online target tracking based on time-difference-of-arrival (TDOA) measurements through a sensor network. Existing pairing designs fix active…
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…
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…
Dynamic Sensor Scheduling Based on Node Partitioning of Graphs
Ryouke Ikura, Junya Hara, Hiroshi Higashi +1
This paper proposes a dynamic sensor scheduling method for sensor networks. In sensor network applications, we often need multiple equally-informative node subsets that are activat…
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…
Joint Graph Estimation and Signal Restoration for Robust Federated Learning
Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi +1
We propose a robust aggregation method for model parameters in federated learning (FL) under noisy communications. FL is a distributed machine learning paradigm in which a central…
Algorithm Unrolling-based Denoising of Multimodal Graph Signals
Hayate Kojima, Keigo Takanami, Junya Hara +4
We propose a denoising method for multimodal graph signals by an alternating minimization scheme that sequentially solves signal restoration and graph learning problems. Many compl…
Multiscale Graph Construction Using Non-local Cluster Features
Reina Kaneko, Hayate Kojima, Kenta Yanagiya +3
This paper presents a multiscale graph construction method using both graph and signal features. Multiscale graph is a hierarchical representation of the graph, where a node at eac…
Time-Varying Graph Signal Estimation among Multiple Sub-Networks
Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi +1
This paper presents an estimation method for time-varying graph signals among multiple sub-networks. In many sensor networks, signals observed are associated with nodes (i.e., sens…
Lossy Compression of Adjacency Matrices by Graph Filter Banks
Kenta Yanagiya, Junya Hara, Hiroshi Higashi +2
This paper proposes a compression framework for adjacency matrices of weighted graphs based on graph filter banks. Adjacency matrices are widely used mathematical representations o…
Optimizing in NN Graphs with Graph Learning Perspective
Asuka Tamaru, Junya Hara, Hiroshi Higashi +2
In this paper, we propose a method, based on graph signal processing, to optimize the choice of in -nearest neighbor graphs (NNGs). NN is one of the most popular appro…
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals
Saki Nomura, Junya Hara, Hiroshi Higashi +1
In this paper, we consider a sensor placement problem where sensors can move within a network over time. Sensor placement problem aims to select K sensor positions from N candidate…
Multi-channel Sampling on Graphs and Its Relationship to Graph Filter Banks
Junya Hara, Yuichi Tanaka
In this paper, we consider multi-channel sampling (MCS) for graph signals. We generally encounter full-band graph signals beyond the bandlimited one in many applications, such as p…
Graph Signal Sampling Under Stochastic Priors
Junya Hara, Yuichi Tanaka, Yonina C. Eldar
We propose a generalized sampling framework for stochastic graph signals. Stochastic graph signals are characterized by graph wide sense stationarity (GWSS) which is an extension o…