Small satellites have limited payload and their attitudes are sometimes difficult to determine from the limited onboard sensors alone. Wrong attitudes lead to inaccurate map projections and measurements that require post-processing correction. In this study, we propose an automated and robust scheme that derives the satellite attitude from its observation images and known satellite position by matching land features from an observed image and from well-registered base-map images. The scheme combines computer vision algorithms (i.e., feature detection, and robust optimization) and geometrical constraints of the satellite observation. Applying the proposed method to UNIFORM-1 observations, which is a 50 kg class small satellite, satellite attitudes were determined with an accuracy of 0.02°, comparable to that of star trackers, if the satellite position is accurately determined. Map-projected images can be generated based on the accurate attitudes. Errors in the satellite position can add systematic errors to derived attitudes. The proposed scheme focuses on determining satellite attitude with feature detection algorithms applying to raw satellite images, unlike image registration studies which register already map-projected images. By delivering accurate attitude determination and map projection, the proposed method can improve the image geometries of small satellites, and thus reveal fine-scale information about the Earth.

Charge state recognition in quantum dot devices is important in the preparation of quantum bits for quantum information processing. Toward auto-tuning of larger-scale quantum devices, automatic charge state recognition by machine learning has been demonstrated. For further development of this technology, an understanding of the operation of the machine learning model, which is usually a black box, will be useful. In this study, we analyze the explainability of the machine learning model estimating charge states in quantum dots by gradient weighted class activation mapping. This technique highlights the important regions in the image for predicting the class. The model predicts the state based on the change transition lines, indicating that human-like recognition is realized. We also demonstrate improvements of the model by utilizing feedback from the mapping results. Due to the simplicity of our simulation and pre-processing methods, our approach offers scalability without significant additional simulation costs, demonstrating its suitability for future quantum dot system expansions.

Accurate attitude information from a satellite image sensor is essential for accurate map projection and reducing computational cost for post-processing of image registration, which enhance image usability, such as change detection. We propose a robust attitude-determination method for pushbroom sensors onboard spacecraft by matching land features in well registered base-map images and in observed images, which extends the current method that derives satellite attitude using an image taken with 2-D image sensors. Unlike 2-D image sensors, a pushbroom sensor observes the ground by changing its position and attitude according to the trajectory of a satellite. To address pushbroom-sensor observation, the proposed method can trace the temporal variation in the sensor attitude by combining the robust matching technique for a 2-D image sensor and a non-linear least squares approach, which can express gradual time evolution of the sensor attitude. Experimental results using images taken from a visible and near infrared pushbroom sensor of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra as test image and Landsat-8/OLI images as a base map show that the proposed method can determine satellite attitude with an accuracy of 0.003° (corresponding to the 2-pixel scale of ASTER) in roll and pitch angles even for a scene in which there are many cloud patches, whereas the determination accuracy remains 0.05° in the yaw angle that does not affect accuracy of image registration compared with the other two axes. In addition to the achieved attitude accuracy that was better than that using star trackers (0.01°) regarding roll and pitch angles, the proposed method does not require any attitude information from onboard sensors. Therefore, the proposed method may contribute to validating and calibrating attitude sensors in space, at the same time better accuracy will contribute to reducing computational cost in post-processing for image registration.

Objective. The emergence of mobile electroencephalogram (EEG) platforms have expanded the use cases of brain–computer interfaces (BCIs) from laboratory-oriented experiments to our daily life. In challenging situations where humans’ natural behaviors such as head movements are unrestrained, various artifacts could deteriorate the performance of BCI applications. This paper explored the effect of muscular artifacts generated by participants’ head movements on the signal characteristics and classification performance of steady-state visual evoked potentials (SSVEPs). Approach. A moving visual flicker was employed to induce not only SSVEPs but also horizontal and vertical head movements at controlled speeds, leading to acquiring EEG signals with intensity-manipulated muscular artifacts. To properly induce neck muscular activities, a laser light was attached to participants’ heads to give visual feedback; the laser light indicates the direction of the head independently from eye movements. The visual stimulus was also modulated by four distinct frequencies (10, 11, 12, and 13 Hz). The amplitude and signal-to-noise ratio (SNR) were estimated to quantify the effects of head movements on the signal characteristics of the elicited SSVEPs. The frequency identification accuracy was also estimated by using well-established decoding algorithms including calibration-free and fully-calibrated approaches. Main results. The amplitude and SNR of SSVEPs tended to deteriorate when the participants moved their heads, and this tendency was significantly stronger in the vertical head movements than in the horizontal movements. The frequency identification accuracy also deteriorated in proportion to the speed of head movements. Importantly, the accuracy was significantly higher than its chance-level regardless of the level of artifact contamination and algorithms. Significance. The results suggested the feasibility of decoding SSVEPs in humans freely moving their head directions, facilitating the real-world applications of mobile BCIs.

Animal responses occur according to a specific temporal structure composed of two states, where a bout is followed by a long pause until the next bout. Such a bout-and-pause pattern has three components: the bout length, the within-bout response rate, and the bout initiation rate. Previous studies have investigated how these three components are affected by experimental manipulations. However, it remains unknown what underlying mechanisms cause bout-and-pause patterns. In this article, we propose two mechanisms and examine computational models developed based on reinforcement learning. The model is characterized by two mechanisms. The first mechanism is choice—an agent makes a choice between operant and other behaviors. The second mechanism is cost—a cost is associated with the changeover of behaviors. These two mechanisms are extracted from past experimental findings. Simulation results suggested that both the choice and cost mechanisms are required to generate bout-and-pause patterns and if either of them is knocked out, the model does not generate bout-and-pause patterns. We further analyzed the proposed model and found that it reproduced the relationships between experimental manipulations and the three components that have been reported by previous studies. In addition, we showed alternative models can generate bout-and-pause patterns as long as they implement the two mechanisms.

Superresolution is an image processing technique that estimates an original high-resolution image from its low-resolution and degraded observations. In superresolution tasks, there have been problems regarding the computational cost for the estimation of high-dimensional variables. These problems are now being overcome by the recent development of fast computers and the development of powerful computational techniques such as variational Bayesian approximation. This paper reviews a Bayesian treatment of the superresolution problem and presents its extensions based on hierarchical modeling by employing hidden variables.