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Welcome to the webpage of the Adaptive Information Processing Group. The group focuses on information processing schemes that adapts to the environment and situation, changing the algorithms and/or the models adaptively. Humans and all living beings have wonderful abilities to solve various problems by trial and error, sometimes spending generations, by using a very limited information processing resource. The main research field of this group is to solve real-world problems, by methods based on learning, optimization and signal processing, devising new algorithms for pattern recognition, signal/image processing and retrieval; the scheme we call the Adaptive Information Processing.

Call for graduation research members 2026 & Open Lab info

(This announcement is intended for undergraduate students already enrolled in Univ. Tsukuba. )

Come to study and implement adaptive information processing abilities found in humans and nature!. All students with fresh ideas and enthusiasm are welcomed.

Open laboratory (2025) :

Tue. Oct. 7th 17:00-18:00 @3E102-1
Fri. Oct. 17th 17:00-18:00 @3E102-1
Thu. Oct. 23rd 17:00-18:00 @3E102-1

Please send to info *at* adapt.cs.tsukuba.ac.jp if you have any questions.
Visits welcomed at any time. Please contact us.

Paper published in International Journal of Remote Sensing

A paper on novel feature extraction method for high-dimensional feature in pattern recognition,

U. A. Md. Ehsan Ali, Pavodi Ndoyi Maniamfu and Keisuke Kameyama,
"Efficient band reduction for hyperspectral imaging with dependency-based segmented principal component analysis"

was published in the International Journal of Remote Sensing, Vol. 45, No. 24, pp. 9311–9337.

Papers presented at IEEE CSPA 2023

The following works have been presented at the 20th IEEE International Colloquium on Signal Processing & Its Applications, held during 3rd-4th March 2023 at Langkawi , Malaysia.

Pavodi Ndoyi Maniamfu and Keisuke Kameyama
“LSTM-based forecasting using policy stringency and time-varying parameters of the SIR model for COVID-19”
Tzu-Jui Huang and Keisuke Kameyama
“Machine Learning Curriculums Generated by Classifier Ensembles”
Takumi Morikawa and Keisuke Kameyama
“CNN Model Compression by Merit-Based Distillation” (Awarded best paper at CSPA)