タブレット端末で用いられるノートアプリケーションのためのメニューデザイン

筑波大学 (University of Tsukuba)201

Inference Algorithms and Sensorimotor Representations in Brains and Machines

Animals function in a 3D world in which survival depends on robust, well-controlled actions. Historically, researchers in Artificial Intelligence (AI) and neuroscience have explored sensory and motor systems independently. There is a growing body of literature in AI and neuroscience to suggest that they actually work in tandem. While there has been a great deal of work on vision and audition as sensory modalities in these fields, one could argue that a more fundamental modality in biology is haptics, or the sense of touch. In this thesis, we will look at building computational models that integrate tactile sensing with other sensory modalities to perform manipulation-like tasks in robots and discrimination tasks in mice. We will also explore the problem of inference through the lens of Markov Chain Monte Carlo methods (MCMC). We elaborate on the ideas discussed in this thesis in the introduction presented in Chapter 1. A challenging problem one often faces when applying probabilistic mathematical models to the study of sensory-motor systems and other problems involving learning of inference is sampling. Hamiltonian Markov Chain Monte Carlo (HMC) algorithms can efficiently draw representative samples from complex probabilistic models. Most MCMC methods rely on detailed balance to ensure that we can sample from the correct distribution. This constraint can be relaxed in discrete state spaces such as those employed by HMC type methods. In Chapter 2, we study HMC methods without detailed balance to explore faster convergence. Markov jump processes are stochastic processes on discrete state space but continuous in time. In Chapter 3, we use Markov Jump Processes to simulate waiting times along with generalized detailed balance. This waiting time ,we show, helps generate samples faster. Most MCMC methods are plagued by slow simulation times on discrete computing systems. In Chapter 4, we explore HMC in analog circuits where the problem of generating samples from a distribution is mapped to the problem of sampling charge in a capacitor.The second half of this dissertation focuses on the role of haptics in perception and action. Manipulation is a fundamental problem for artificial and biological agents. High dimensional actuators (say, fingers, trunks,etc) are really hard to control. In Chapter 5, we present an approach to learn to actuate dexterous manipulators to grasp objects in simulation. Haptics as a sensory modality is critical to many manipulation tasks. Employing haptics in high dimensional dextrous actuators is challenging. In Chapter 6, we explore how intrinsic curiosity and haptics can be used to learn exploration strategies for discrimination of objects with dextrous hands. A key component to make tactile sensing a possibility is the availability of cheap, efficient, scalable hardware. Chapter 7 presents results for tactile servoing using a physical gelsight sensor. Traditional neuroscience texts delineate sensory and motor systems as two independent systems yet recent results suggest that this may not be entirely complete. That is, there is evidence to suggest that the representations in the cortex is more distributed than is accepted. Finally in Chapter 8, we explore building a computational model of spiking neural data collected from both the barrel and motor cortices during free and active whisking. These works help towards understanding sensorimotor representations in the context of haptics and high dimensional controls. We conclude with a discussion on future directions in Chapter 9

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Behaviour-aware mobile touch interfaces

Mobile touch devices have become ubiquitous everyday tools for communication, information, as well as capturing, storing and accessing personal data. They are often seen as personal devices, linked to individual users, who access the digital part of their daily lives via hand-held touchscreens. This personal use and the importance of the touch interface motivate the main assertion of this thesis: Mobile touch interaction can be improved by enabling user interfaces to assess and take into account how the user performs these interactions. This thesis introduces the new term "behaviour-aware" to characterise such interfaces. These behaviour-aware interfaces aim to improve interaction by utilising behaviour data: Since users perform touch interactions for their main tasks anyway, inferring extra information from said touches may, for example, save users' time and reduce distraction, compared to explicitly asking them for this information (e.g. user identity, hand posture, further context). Behaviour-aware user interfaces may utilise this information in different ways, in particular to adapt to users and contexts. Important questions for this research thus concern understanding behaviour details and influences, modelling said behaviour, and inference and (re)action integrated into the user interface. In several studies covering both analyses of basic touch behaviour and a set of specific prototype applications, this thesis addresses these questions and explores three application areas and goals: 1) Enhancing input capabilities – by modelling users' individual touch targeting behaviour to correct future touches and increase touch accuracy. The research reveals challenges and opportunities of behaviour variability arising from factors including target location, size and shape, hand and finger, stylus use, mobility, and device size. The work further informs modelling and inference based on targeting data, and presents approaches for simulating touch targeting behaviour and detecting behaviour changes. 2) Facilitating privacy and security – by observing touch targeting and typing behaviour patterns to implicitly verify user identity or distinguish multiple users during use. The research shows and addresses mobile-specific challenges, in particular changing hand postures. It also reveals that touch targeting characteristics provide useful biometric value both in the lab as well as in everyday typing. Influences of common evaluation assumptions are assessed and discussed as well. 3) Increasing expressiveness – by enabling interfaces to pass on behaviour variability from input to output space, studied with a keyboard that dynamically alters the font based on current typing behaviour. Results show that with these fonts users can distinguish basic contexts as well as individuals. They also explicitly control font influences for personal communication with creative effects. This thesis further contributes concepts and implemented tools for collecting touch behaviour data, analysing and modelling touch behaviour, and creating behaviour-aware and adaptive mobile touch interfaces. Together, these contributions support researchers and developers in investigating and building such user interfaces. Overall, this research shows how variability in mobile touch behaviour can be addressed and exploited for the benefit of the users. The thesis further discusses opportunities for transfer and reuse of touch behaviour models and information across applications and devices, for example to address tradeoffs of privacy/security and usability. Finally, the work concludes by reflecting on the general role of behaviour-aware user interfaces, proposing to view them as a way of embedding expectations about user input into interactive artefacts

Image Processing and Simulation Toolboxes of Microscopy Images of Bacterial Cells

Recent advances in microscopy imaging technology have allowed the characterization of the dynamics of cellular processes at the single-cell and single-molecule level. Particularly in bacterial cell studies, and using the E. coli as a case study, these techniques have been used to detect and track internal cell structures such as the Nucleoid and the Cell Wall and fluorescently tagged molecular aggregates such as FtsZ proteins, Min system proteins, inclusion bodies and all the different types of RNA molecules. These studies have been performed with using multi-modal, multi-process, time-lapse microscopy, producing both morphological and functional images. To facilitate the finding of relationships between cellular processes, from small-scale, such as gene expression, to large-scale, such as cell division, an image processing toolbox was implemented with several automatic and/or manual features such as, cell segmentation and tracking, intra-modal and intra-modal image registration, as well as the detection, counting and characterization of several cellular components. Two segmentation algorithms of cellular component were implemented, the first one based on the Gaussian Distribution and the second based on Thresholding and morphological structuring functions. These algorithms were used to perform the segmentation of Nucleoids and to identify the different stages of FtsZ Ring formation (allied with the use of machine learning algorithms), which allowed to understand how the temperature influences the physical properties of the Nucleoid and correlated those properties with the exclusion of protein aggregates from the center of the cell. Another study used the segmentation algorithms to study how the temperature affects the formation of the FtsZ Ring. The validation of the developed image processing methods and techniques has been based on benchmark databases manually produced and curated by experts. When dealing with thousands of cells and hundreds of images, these manually generated datasets can become the biggest cost in a research project. To expedite these studies in terms of time and lower the cost of the manual labour, an image simulation was implemented to generate realistic artificial images. The proposed image simulation toolbox can generate biologically inspired objects that mimic the spatial and temporal organization of bacterial cells and their processes, such as cell growth and division and cell motility, and cell morphology (shape, size and cluster organization). The image simulation toolbox was shown to be useful in the validation of three cell tracking algorithms: Simple Nearest-Neighbour, Nearest-Neighbour with Morphology and DBSCAN cluster identification algorithm. It was shown that the Simple Nearest-Neighbour still performed with great reliability when simulating objects with small velocities, while the other algorithms performed better for higher velocities and when there were larger clusters present