We present ZeRONE, a new indoor drone that does not use rotating blades for propulsion. The proposed device is a helium blimp type drone that uses the wind generated by the ultrasonic vibration of piezo elements for propulsion. Compared to normal drones with rotating propellers, the drone is much safer because its only moving parts are the piezo elements whose surfaces vibrate at the order of micrometers. The drone can float for a few weeks and the ultrasonic propulsion system is quiet. We implement a prototype of the drone and evaluate its performance and unique characteristics in experiments. Moreover, application scenarios in which ZeRONE coexists with people are also discussed.

The authors aim to develop a system that saves money by providing tips (proposed actions; the tips are called know-how) based on user preferences. In order to select helpful tips, this study develops a method that improves estimation accuracy by optimizing the tip indices that represent the tendency in user preferences. Concretely, the method optimizes the tip indices using both the result generally acquired by five-level evaluations and the result gradually acquired by a paired comparison method in which the users are requested to select one of pair of tips. Evaluation experiments show the general trend that estimation accuracy rises with the amounts of paired comparison data.

This paper proposes a method that predicts customer value by focusing on purchasing behavior. The method generates a relevance model for purchase days and amount in each period between customer value and purchasing histories beforehand based on a consumer panel survey. The authors adopt the random forest method to generate the prediction model. The proposed method facilitates the provisioning of smart customer management to each customer according to level such as suggesting products or services.

We present CamTrackPoint, a new input interface that can be controlled by finger gestures captured by front or rear cameras of a mobile device. CamTrackPoint mounts a 3D-printed ring on the camera's bezel, and it senses the movements of the user's finger by tracking the light passed through the finger. The proposed method provides mobile devices with a new input interface that offers physical force feedback like a pointing stick. The cost of our method is low as it needs only a simple ring-shaped part on the camera bezel. Moreover, the ring doesn't disturb the functions of the camera, unless a user uses the interface. We implement a prototype for a smartphone; two CamTrackPoint rings are made for the front and rear cameras. We evaluate its performance and characteristics in an experiment. The proposed technique provides smooth scrolling and would give better game experience on the available smartphone.

This paper proposes a method that estimatesconsumer family-structure attributesby focusing on purchasing behavior. The method generatesa relevance model for each product type between family-structure attributes and purchasing histories beforehand based on a consumer panel survey. Random Forest, a machine learning method, is employed to generate the model. The proposed method facilitates provisioning of smart recommendations to the consumer family such as suggesting products that reflectthe family structure attributes of the consumer.

In Japan, which has become a very aged society, the increasing burden of nursing care is an issue. Services and systems related to automatic recording of healthcare management of elderly people have been proposed in order to reduce the burden of nursing care. Water intake is one of the items necessary for healthcare management of elderly people. However, it is not currently automated, which is a burden on caregivers. In the case of the conventional method, the swallowing sound is used for estimating the water intake. However, the estimation error for each subject is large. Accuracy of estimated water intake is improved by using deep learning. Specifically, three features, namely, mel frequency cepstral coefficient (MFCC), duration of water intake, and a RASTA filter auditory spectrum, are extracted from a subject's swallowing sound (which is thought to be highly correlated with water intake). A method of estimating water intake, which considers abstract features that are difficult for people to find, is proposed and verified. It is experimentally shown that RMSE of water intake estimated by the proposed method using deep learning is reduced compared with that estimated by conventional methods.

We present iSphere, a flying spherical display that can display high resolution and bright images in all directions from anywhere in 3D space. Our goal is to build a new platform which can physically and directly emerge arbitrary bodies in the real world. iSphere flies by itself using a built-in drone and creates a spherical display by rotating arcuate multi light-emitting diode (LED) tapes around the drone. As a result of the persistence of human vision, we see it as a spherical display flying in the sky. The proposed method yields large display surfaces, high resolution, drone mobility, high visibility and 360° field of view. Previous approaches fail to match these characteristics, because of problems with aerodynamics and payload. We construct a prototype and validate the proposed method. The unique characteristics and benefits of flying spherical display surfaces are discussed and we describe application scenarios based on iSphere such as guidance, signage and telepresence.