This paper presents a design, a control scheme, and experiments for a new vibrating magnet robot (ViMbot). Permanent magnets in the new robot vibrate due to an external vibrating magnetic field. The vibration motion of the robot induces the locomotion of the robot. The vibratory dynamic response of the ViMbot is heavily dependent on the frequency of the vibration. This paper shows that the direction of the robot locomotion can be controlled by the vibration frequency of the external magnetic field. To achieve the distinct dynamic responses depending on the vibration frequency, three acrylic plates with different torsional stiffness are attached at three sides of the triangle-shaped ViMbot and then a magnet is attached to each plate. A strategy to find the special frequencies inducing locomotion and associated experiments with the ViMbot are presented. Control experiments for the ViMbot using the special frequencies are also presented. Because the proposed robot is actuated by an external energy source, the robot can move without traditional actuation components such as motors with batteries. This new design concept allows us to miniaturize the proposed robot.

In this paper we design a robot that can move forward and backward based on the vibration excitation. The proposed design enables the robot to locomote on the horizontal surface using the vibration excitation that is perpendicular to the locomotion direction. The bidirectional locomotion is achieved, and the direction of motion is determined by the vibration frequency. This new type of robot is useful for the unconventional environment where complex moving mechanisms cannot be applied to the robots such as medical, small pipeline, and underwater vehicle applications. To this end, two springs with different stiffness are perpendicularly connected and support the main body of the robot. The dynamic response of this nonlinear system to the vibration enables the robot to walk forward and backward depending on the vibration frequency. The numerical simulation of this system and the experiments with actual hardware design verify the proposed approach.

In this paper, we improve the path-of-probability (POP) algorithm to generate better and smoother paths for robotic systems. The POP algorithm generates a full path with the maximum probability to reach a target. Although the POP algorithm has been successfully applied to path generation of robotic systems, it has the limit that the intermediate paths are generated using a finite and small number of candidate paths. This results in non-smooth (or zigzag pattern) paths which are not desirable for practical use. In addition, this approach sometimes fails to generate a path that reaches a target. This can be overcome by increasing the number of candidate paths, but it increases the computation time too. To improve the POP method, we use a fuzzy logic. The fuzzy logic enables us to obtain a continuous set for the intermediate paths from the discrete candidate paths. By applying it, smoother paths from the starting point to the target can be obtained directly without any additional path smoothing because the intermediate paths are chosen from a continuous set. Fundamentally, the fuzzy logic makes a mapping from a finite and discrete set of candidate paths to a continuous set of paths. We apply the proposed method to path planning for a two wheeled robot and a flexible needle. The simulation results confirm the performance of the proposed approach.

In single-particle electron microscopy (EM), multiple micrographs of identical macromolecular structures or complexes are taken from various viewing angles to obtain a 3D reconstruction. A high-quality EM reconstruction typically requires several thousand to several million images. Therefore, an automated pipeline for performing computations on many images becomes indispensable. In this paper, we propose a modified cross-correlation method to align a large number of images from the same class in single-particle electron microscopy of highly nonspherical structures, and show how this method fits into a larger automated pipeline for the discovery of 3D structures. Our modification uses a probability density in full planar position and orientation, akin to the pose densities used in Simultaneous Localization and Mapping (SLAM) and Assembly Automation. Using this alignment and a subsequent averaging process, high signal-to-noise ratio (SNR) images representing each class of viewing angles are obtained for reconstruction algorithms. In the proposed method, first we coarsely align projection images, and then realign the resulting images using the cross correlation (CC) method. The coarse alignment is obtained by matching the centers of mass and the principal axes of the images. The distribution of misalignment in this coarse alignment is estimated using the statistical properties of the additive background noise. As a consequence, the search space for realignment in the CC method is reduced. Additionally, in order to overcome the false peak problems in the CC, we use artificially blurred images for the early stage of the iteration and segment the intermediate result from every iteration step. The proposed approach is demonstrated on synthetic noisy images of GroEL/ES.

In this paper, we propose a new insertion plan for steerable flexible needles with which we can target multiple locations in the plane with a single entry point (i.e. port). The method is developed based on the observation that multiple locations can be reached by a flexible needle through insertion, partial retraction, rotation, and re-insertion of the needle. We show that in 2D space this problem can be solved using a geometric relationship between multiple tangent circles. Specifically we find a needle insertion point, a corresponding insertion direction and lengths for insertion and retraction with which we can generate the optimal needle trajectory that reaches two or three planar targets with the minimum tissue damage. This minimization problem is solved using exhaustive search of a cost function on the 1D bounded domain. We build a prototype of a needle insertion system and develop C#-based software to compute the optimal needle paths and perform the planned insertion as an open-loop controller. Finally, actual insertion examples are presented.

Using the cloud storage services, users can access their data in any time, at any place, even with any computing device including mobile devices. Although these properties provide flexibility and scalability in handling data, security issues should be handled especially when mobile devices try to access data stored in cloud storage. Currently, a typical cloud storage service, Dropbox, offers server-side data encryption for security purpose. However, we think such method is not secure enough because all the encryption keys are managed by software and there is no attestation on the client software integrity. Moreover, a simple user identification based on user ID and Password is also easy to be compromised. Data sharing which is critical in enterprise environment is significantly restricted because it is not easy to share encryption key among users. In this paper, we propose DFCloud, a secure data access control method of cloud storage services to handle these problems found in the typical cloud storage service Dropbox. DFCloud relies on Trusted Platform Module (TPM) [1] to manage all the encryption keys and define a key sharing protocol among legal users. We assume that each client is mobile device using ARM TrustZone [2] technology. The DFCloud server prototype is implemented using ARM Fastmodel 7.1 and Open Virtualization software stack for ARM TrustZone. For DFCloud client, TPM functions are developed in the secure domain of ARM TrustZone because most ARM-based mobile devices are not equipped with TPM chip. The DFCloud framework defines TPM-based secure channel setup, TPM-based key management, remote client attestation, and a secure key share protocol across multiple users/devices. It is shown that our concept works correctly through a prototype implementation.

Super resolution (SR) image reconstruction is used to obtain a high resolution (HR) image from multiple low resolution (LR) images. In this paper, we propose a new method to solve the SR reconstruction problem. The LR images are modeled as downsampled images of the original scene shifted by sub-pixel distances. In addition, we model the downsampling process as averaging light intensity on the corresponding pixel area. Based on this downsampling model, the average of multiple LR images with appropriate registration can be thought of as a blurred version of the HR image. After the point spread function (PSF) for this blur is identified, the HR image is obtained by regularized deconvolution method. The regularization factor can be determined by line search of a cost function. Two distinct cases are considered: (1) only translational motion among LR images is assumed, (2) both translational and rotational motions among LR images are considered. For the second case, the rigid body group representation is used.

Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%.

In this paper, we propose an approach for the accurate rotation of a digital image using Hermite expansions. This exploits the fact that if a 2-D continuous bandlimited Hermite expansion is rotated, the resulting function can be expressed as a Hermite expansion with the same bandlimit. Furthermore, the Hermite coefficients of the initial 2-D expansion and the rotated expansion are mapped through an invertible linear relationship. Two efficient methods to compute the mapping between Hermite coefficients during rotation are proposed. We also propose a method for connecting the Hermite expansion and a discrete image. Using this method, we can obtain the Hermite expansion from a discrete image and vice versa. Combining these techniques, we propose new methods for the rotation of discrete images. We assess the accuracy of our methods and compare them with an existing FFT-based method implementing three shears. We find that the method proposed here consistently has better accuracy than the FFT-based method.

In this paper, we propose an algorithm for lossless conversion of data between Cartesian and polar coordinates, when the data is sampled from a 2-D real-valued function (a mapping: R2 --> R) expressed as a particular kind of truncated expansion. We use Laguerre functions and the Fourier basis for the polar coordinate expression. Hermite functions are used for the Cartesian coordinate expression. A finite number of coefficients for the truncated expansion specifies the function in each coordinate system. We derive the relationship between the coefficients for the two coordinate systems. Based on this relationship, we propose an algorithm for lossless conversion between the two coordinate systems. Resampling can be used to evaluate a truncated expansion on the complementary coordinate system without computing a new set of coefficients. The resampled data is used to compute the new set of coefficients to avoid the numerical instability associated with direct conversion of the coefficients. In order to apply our algorithm to discrete image data, we propose a method to optimally fit a truncated expression to a given image. We also quantify the error that this filtering process can produce. Finally the algorithm is applied to solve the polar-Cartesian interpolation problem.

Fine needles facilitate diagnosis and therapy because they enable minimally invasive surgical interventions. This paper formulates the problem of steering a very flexible needle through firm tissue as a nonholonomic kinematics problem, and demonstrates how planning can be accomplished using diffusion-based motion planning on the Euclidean group, SE(3). In the present formulation, the tissue is treated as isotropic and no obstacles are present. The bevel tip of the needle is treated as a nonholonomic constraint that can be viewed as a 3D extension of the standard kinematic cart or unicycle. A deterministic model is used as the starting point, and reachability criteria are established. A stochastic differential equation and its corresponding Fokker-Planck equation are derived. The Euler-Maruyama method is used to generate the ensemble of reachable states of the needle tip. Inverse kinematics methods developed previously for hyper-redundant and binary manipulators that use this probability density information are applied to generate needle tip paths that reach the desired targets.