Prof. Arif Sasongko

Institut Teknologi Bandung, Indonesia

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ABSTRACT

BIOGRAPHY

Prof. Seishi Takamura, Ph.D.

NTT.Inc. / Hosei University, Japan

FROM NOISE TO KNOWLEDGE: SCIENTIFIC INSIGHTS INTO SENSOR IMAGING

ABSTRACT

In everyday life, we rely on cameras to capture images and videos, often assuming that what we see reflects the true nature of the scene. Yet, light is composed of photons whose arrivals are inherently stochastic and time-varying—even under constant illumination. This randomness introduces noise into sensor measurements, frame by frame.

Fortunately, photon arrivals follow a well-defined statistical model: the Poisson distribution. Leveraging this property enables us to recover true image brightness beyond the well capacity limitations of conventional sensors. It also facilitates advanced applications such as precise sensor calibration and photon-counting accuracy in SPAD (Single-Photon Avalanche Diode) sensors.

This research has further contributed to breakthroughs in film grain modeling, a topic of renewed interest among analog photography enthusiasts. These findings underpin the scientific rationale for incorporating film grain mechanisms into international video coding standards, including H.266/VVC.

BIOGRAPHY

Dr. Seishi Takamura is a Visiting Distinguished Senior Researcher at NTT, Inc. and a Professor at Hosei University, Japan. He received his B.E., M.E., and Ph.D. degrees in Electronic Engineering from the University of Tokyo in 1991, 1993, and 1996, respectively. In 1996 he joined NTT and in 2022 he joined Hosei University. His research focuses on efficient video coding and multi-modal signal processing.

Dr. Takamura has held numerous leadership roles in the academic and technical community. He served as Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology (2006–2014), Editor-in-Chief of the Institute of Image Information and Television Engineers (ITE, 2015–2017), and Editor-in-Chief of APSIPA Transactions on Signal and Information Processing (2026–). He has also served as an Executive Committee Member of IEEE Region 10 and the IEEE Japan Council, Chair of the Japan National Body, and Head of Delegation for ISO/IEC JTC 1/SC 29 (2011–2021). From 2005 to 2006, he was a Visiting Scientist at Stanford University.

He has received 78 academic awards, including the ITE Niwa-Takayanagi Awards (Best Paper, 2002; Achievement, 2017), the IPSJ Nagao Special Researcher Award (2006), the ITE Fujio Frontier Award (2014), and the TAF Telecom System Technology Awards (2004, 2008, 2015). He also received the IEICE 100-Year Memorial Best Paper Award (2017), the Distinguished Achievement and Contributions Award (2025), the Kenjiro Takayanagi Achievement Award (2019), the Industrial Standardization Merit Award from Japan’s Ministry of Economy, Trade and Industry (2019), and the IPSJ/ITSCJ Standardization Achievement Award (2022).

Dr. Takamura is a Fellow of IEEE, IEICE, IPSJ, ITE, AIAA, and AIIA. He is a member of Japan MENSA, IIEEJ (President 2024–2026), and a life member of APSIPA (Vice President 2020–2023).

Prof. Akira Hirose

The University of Tokyo, Japan

ADVANCED AI FOR EARTH OBSERVATION IN THE FORTHCOMING MASSIVE DATA ERA

ABSTRACT

The number of Earth observation satellites is rapidly increasing. Their observation data will become dramatically dense in time and space, enriching data of polarimetric, interferometric, and polarimetric0interferometric synthetic aperture radars. This will also drive advances in the use of artificial intelligence (AI) in data collection and initial processing. In this keynote speech, we will take up quaternion reservoir computing, outline its features and effectiveness, and look ahead to future developments in Earth observation in the massive data era.

BIOGRAPHY

Dr. Konrad Young

National Taiwan University of Science and Technology, Taiwan

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ABSTRACT

BIOGRAPHY

Dr. Konrad Young obtained his doctoral degree in Electrical Engineering and Computer Science from the University of California, Berkeley. When in the United States, he served as a researcher at the Lincoln Laboratory of the Massachusetts Institute of Technology (MIT) and as a senior technologist at HP. After leaving the United States, he successively worked at Chartered Semiconductor, Winbond Electronics, and Worldwide Semiconductor. From 1998 to 2005, he served as the R&D Director at TSMC, responsible for developing advanced processes at 0.18-micron, 0.13-micron, and 65-nanometer. In 2005, he went to the United States to oversee TSMC’s research and development program and advanced technology customer collaboration projects. In 2012, he transferred to TSMC’s Basic Engineering Department and the Office of Advanced Technology Management.

In 2001, TSMC began mass production of the 0.13-micron process, laying the foundation for TSMC’s later significant lead over its competitors. Several key figures involved in the development efforts were later hailed as the “Six Knights of TSMC’s R&D,” with Dr. Young being one of them responsible for integrating the overall logic process.

After retiring from TSMC, Dr. Young served as an independent SMIC director and technical advisor to Intel. He is currently a CEO in the College of Industry-Academia Innovation at National Taiwan University of Science and Technology and is deeply passionate about talent development issues.

Artificial Intelligence of Things (AIoT), is the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), combining the data-collecting capabilities and connectivity of IoT devices with the analytical and decision-making powers of AI. By embedding lightweight machine-learning intelligence into connected sensors, actuators, and wearable devices, AIoT enables real-time analytics in smart homes and factories, predictive maintenance in energy and transportation, personalized healthcare and eldercare, environmental monitoring for climate-resilient agriculture, and adaptive infrastructure for future smart cities. These diverse use cases share a common technical challenge: stringent constraints on power, latency, reliability, security, and cost that can only be met through innovative circuit and system-level co-design.

This special session invites contributions that advance the state of the art in integrated-circuit (IC) and system architectures tailored to AIoT workloads. Topics include, but are not limited to low-power digital/analog/mixed-signal circuits for on-device learning and inference, reconfigurable and heterogeneous system-on-chip (SoC) combining CPUs, GPUs, FPGAs, and TPU for edge intelligence, hardware-software (HW-SW) co-optimization for rapid IoT prototyping, and case studies demonstrating end-to-end AIoT systems.

Quantum information processing offers new opportunities for tasks like image recognition, classification, and complex pattern analysis by leveraging quantum algorithms such as Quantum Machine Learning (QML), Quantum Fourier Transform (QFT), and other emerging quantum techniques. These approaches promise faster and more efficient data processing for applications in signal processing, communications, and AI.

This special session invites contributions on developing and applying diverse quantum algorithms—including variational circuits, quantum search, and optimization methods—for tasks such as image and signal classification, feature extraction, and secure communications, as well as demonstrations of practical quantum-assisted systems.

As the digital era accelerates, the demand for more personalized, context-aware, and intelligent systems continues to grow. This special session (SS) explores the development and application of hyper-personalized artificial intelligence (AI) technologies that adapt to individual user preferences, behaviors, and real-time contexts.

By leveraging advanced machine learning algorithms, multimodal data processing, and edge computing, hyper-personalized AI enables more responsive and tailored experiences across domains such as education, healthcare, e-commerce, and smart living. We also present case studies and future directions, emphasizing how hyper-personalized AI can redefine user engagement and shape the digital future.

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Greetings, ISPACS Participants!

And welcome to Indonesia to all the international participants. If you come to Indonesia through Soekarno Hatta International Airport, this is our recommended method to reach Bandung

1. SOEKARNO HATTA INTERNATIONAL AIRPORT

After your safe landing in Terminal 3 and claiming your baggage, you can find our partner Blue Bird Group just outside the baggage claim area by looking for ISPACS 2025 on the counter’s name.

2. BLUE BIRD X ISPACS 2025 TICKET COUNTER

You may show your participant’s number and identification. The clerk will offer two options: a direct route to Bandung City, or connecting route via Whoosh High Speed Rail. Don’t be shy to ask any question regarding the trip.

3. ON THE ROAD

If you choose direct route, you will be taken directly to Bandung City. If you choose connecting route, you will be taken to Whoosh Station Jakarta at Halim.

4. WHOOSH STATION JAKARTA HALIM

You can buy Whoosh ticket at the station’s ticket counter. Inform the clerk that your destination is the Padalarang Station. Get on your train and enjoy the ride on our country’s first High Speed Rail.

5. WHOOSH STATION PADALARANG

Make sure that you get off on Padalarang Station as you have to continue your journey using feeder train that will take you from Padalarang Station to Bandung Station in Bandung City.

6. BANDUNG CITY

Welcome to Bandung Kota Kembang where ISPACS 2025 will be held. You can join us on Day 1, November 4th to attend the Welcome Reception. Don’t forget to visit tourist attractions and enjoy your stay here.

Other Information

If you would like to know other transportation method or other inquiries, you can contact us at: ispacs2025@staff.stei.itb.ac.id

Download travel flyer.