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The disinfection of environmental surfaces is an effective countermeasure for COVID-19. In this paper, we use a thermographic camera and lightweight background image processing and propose a method that detects and visualizes the places touched by a person. Our method will support effective disinfection activities.

In this paper, we propose PASNIC, a lightweight privacy-aware sensor node for image capturing. PASNIC masks the region of faces and private screens (smartphone and portable computer displays) in captured visible images before sending them to remote sites. Thermal information, which can also be captured with a thermal image sensor, is used to detect the regions of faces and private screens. Our masking algorithms are so simple that even sensor nodes with low processing power can sufficiently process them.

We propose a method that interpolates missing values from sensor nodes in a sensor network using Nonnegative Matrix Factorization. Since nearby sensor nodes take approximate values, more reliable interpolation is possible with these values. We carried out experiments and evaluations using the data of sensors deployed in a real environment.

Garbage is intricately with our daily life. We are investigating method that estimates regional amounts of garbage using motion sensors mounted on garbage trucks. In this paper, we report out analysis results of garbage amounts using national census data. We could obtain insightful information from just motion sensors we mounted on garbage trucks.

We have developed a network-type brain machine interface (BMI) system called "Network BMI" to help elderly and physically handicapped people to become self-reliant in daily life such as at home or a health clinic. It can control real-world actuators like wheelchairs based on human intention measured by portable brain measurement devices. In this paper, we introduce the technologies for achieving the Network BMI system to support activities of daily living.

We propose a method for selecting home appliances using a smart glass, which facilitates the control of network-connected appliances in a smart house. Our proposed method is image-based appliance selection and enables smart glass users to easily select a particular appliance by just looking at it. The main feature of our method is that it achieves high precision appliance selection using user contextual information such as position and activity, inferred from various sensor data in addition to camera images captured by the glass because such contextual information is greatly related in the home appliance that a user wants to control in her daily life. We design a state-of-the-art appliance selection method by fusing image features extracted by deep learning techniques and context information estimated by non-parametric Bayesian techniques within a framework of multiple kernel learning. Our experimental results, which use sensor data obtained in an actual house equipped with many network-connected appliances, show the effectiveness of our method.

We introduce a new data compression method for efficient data gathering in hierarchical sensor networks. Our proposed method compresses sensor data sequences by decomposing them into local patterns and weight variables using Singular Value Decomposition (SVD). Our proposed method can achieve efficient data gathering for environmental monitoring.

In this paper, we develop new protocols for a combinatorial, multi-attribute procurement auction in which each sales item (task) is defined by several attributes called quality, the buyer is the auctioneer (e.g., a government), and the sellers are the bidders. Furthermore, there exist multiple tasks, and both buyer and sellers can have arbitrary (e.g., complementary/substitutable) preferences on a bundle of tasks. In this setting, there is a chance that a VCG protocol cannot satisfy Individual Rationality (IR) for the buyer, i.e., the buyer’s utility can be negative. We show that if a surplus function is concave, then the VCG protocol satisfies IR and the protocol is also false-name-proof, i.e., using multiple identifiers provides no advantage. Furthermore, we present a modification of the VCG protocol that satisfies IR even if the concavity condition is not satisfied. The key idea of this protocol is to introduce a special type of bidder called the reference bidder. We assume that the auctioneer knows the upper-bound of the reference bidder’s cost. Introducing such a reference bidder is similar to setting reservation prices in standard combinatorial auctions. Furthermore, we develop a new false-name-proof protocol that is based on the idea of the Leveled Division Set (LDS) protocol.