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.

Sensor networks allow us to collect data, such as camera images, over a wide area. Understanding the sensing area by aggregating and processing the data from multiple sensors is promising. Deep Learning (DL) is a powerful method for interpreting data. However, communication cost on the sensor network and computational cost on the server are two substantial problems of aggregating and processing data from multiple sensors. We therefore propose divided processing of the DL between a server and a powerful Edge-Heavy Sensor (EHS). In our study, we reduced transmission data to twelve times lower than the amount of raw input data while maintaining a 4.5% decrease in the DL's recognition accuracy.

Retrieving past egocentric videos about personal daily life is important to support and augment human memory. Most previous retrieval approaches have ignored the crucial feature of human-physical world interactions, which is greatly related to our memory and experience of daily activities. In this paper, we propose a gesture-based egocentric video retrieval framework, which retrieves past visual experience using body gestures as non-verbal queries. We use a probabilistic framework based on a canonical correlation analysis that models physical interactions through a latent space and uses them for egocentric video retrieval and re-ranking search results. By incorporating physical interactions into the retrieval models, we address the problems resulting from the variability of human motions. We evaluate our proposed method on motion and egocentric video datasets about daily activities in household settings and demonstrate that our egocentric video retrieval framework robustly improves retrieval performance when retrieving past videos from personal and even other persons' video archives.

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.

This paper reports on an innovative approach for solving satisfiability problems for propositional formulas in conjunctive normal form (SAT) by creating a logic circuit that is specialized to solve each problem instance on field programmable gate arrays (FPGAs). This approach has become feasible due to recent advances in reconfigurable computing and has opened up an exciting new research field in algorithm design. SAT is an important subclass of constraint satisfaction problems, which can formalize a wide range of application problems. We have developed a series of algorithms that are suitable for a logic circuit implementation, including an algorithm whose performance is equivalent to the Davis-Putnam procedure with powerful dynamic variable ordering. Simulation results show that this method can solve a hard random 3-SAT problem with 400 variables within 1.6 min at a clock rate of 10 MHz. Faster speeds can be obtained by increasing the clock rate. Furthermore, we have actually implemented a 128-variable 256-clause problem instance on FPGAs.

This paper presents an approach to performing applications using reconfigurable computing (RC). Our RC approach is achieved by effective use of design automation systems. Logic circuits specialized for each individual application task are automatically implemented on FPGAs. Such circuits can quickly perform tasks that are time-consuming for general purpose computers. Decoding of binary linear block codes for the evaluation is taken up as an example application. Experimental results show that the time for decoding of the code specific decoding circuit implemented on FPGAs, in which computations are executed in parallel, is much shorter than that of the software decoder.

This paper presents a logic synthesis method for look-up table (LUT) based field programmable gate arrays (FPGAs). We determine functions to be mapped to LUTs by functional decomposition. We use not only disjunctive decomposition but also nondisjunctive decomposition. Furthermore, we propose a new Boolean resubstitution technique customized for an LUT network synthesis. Resubstitution is used to determine whether an existing function is useful to realize another function; thus, we can share the common function among two or more functions. The Boolean resubstitution is effectively carried out by solving a support minimization problem for an incompletely specified function. We can also handle satisfiability don't cares of an LUT network using the technique.