Earth Observation via Compressive Sensing: The Effect of Satellite Motion

In the framework of earth observation for scientific purposes, we consider a multiband spatial compressive sensing (CS) acquisition system, based on a pushbroom scanning. We conduct a series of analyses to address the effects of the satellite movement on its performance in a context of a future space mission aimed at monitoring the cryosphere. We initially apply the state-of-the-art techniques of CS to static images, and evaluate the reconstruction errors on representative scenes of the earth. We then extend the reconstruction algorithms to pushframe acquisitions, i.e., static images processed line-by-line, and pushbroom acquisitions, i.e., moving frames, which consider the payload displacement during acquisition. A parallel analysis on the classical pushbroom acquisition strategy is also performed for comparison. Design guidelines following this analysis are then provided.

Dynamics of machines with ideal inertial motion

The term "inertioid" and its first design in 1936 was invented by engineer V. N. Tolchin. Despite the demonstration of unsupported motion using a physical model, the mystery of the inertioid has existed for almost a century. There are several theories explaining the motion of the inertioid (or mechanisms with inertial motion). These theories include the theory of friction, which proves that the movement of the device occurs due to the difference between the coefficients of friction and the coefficients of rolling resistance in contact between the bottom of the machine and the road. In some works, to explain the physical nature of this phenomenon, it is often legitimate to use A. Einstein's theory of relativity from a scientific point of view. In our opinion, the approach to the study of the process of motion of the inertioid should be based on the theory of the gravitational field. In the theory of relativity, A. Einstein notes that rapidly moving frames of reference create their own gravitational fields. Rotating weights create their own potential fields, since they are affected by centripetal accelerations. When the field of rotating loads is imposed on the gravitational field of the earth, accelerations appear that cause the movement of an inertioid (machines with an inertial mover). In fact, we constantly encounter this kind of overlap of potential fields in our daily life. For example, the effect of latitude on the value of the free fall acceleration of a body above the earth's surface is explained by the imposition of the earth's gravitational field of the potential field of its rotation around its axis. In the paper an inertioid with an idealized engine, which creates a constant driving (traction) force directed towards the movement has been investigated. As a result of the study, the equations of the translational motion of a machine with an ideal inertial engine were obtained, an expression for calculating its maximum speed was determined, and the maximum required engine power for the movement of a machine with an ideal inertial engine was determined.

Analysis and control of a Stewart platform as base motion compensators - Part I: Kinematics using moving frames

Kinematics and its control application are presented for a Stewart platform whose base plate is installed on a floor in a moving ship or a vehicle. With a manipulator or a sensitive equipment mounted on the top plate, a Stewart platform is utilized to mitigate the undesirable motion of its base plate by controlling actuated translational joints on six legs. To reveal closed loops, a directed graph is utilized to express the joint connections. Then, kinematics begins by attaching an orthonormal coordinate system to each body at its center of mass and to each joint to define moving coordinate frames. Using the moving frames, each body in the configuration space is represented by an inertial position vector of its center of mass in the three-dimensional vector space ℝ3, and a rotation matrix of the body-attached coordinate axes. The set of differentiable rotation matrices forms a Lie group: the special orthogonal group, SO(3). The connections of body-attached moving frames are mathematically expressed by using frame connection matrices, which belong to another Lie group: the special Euclidean group, SE(3). The employment of SO(3) and SE(3) facilitates effective matrix computations of velocities of body-attached coordinate frames. Loop closure constrains are expressed in matrix form and solved analytically for inverse kinematics. Finally, experimental results of an inverse kinematics control are presented for a scale model of a base-moving Stewart platform. Dynamics and a control application of inverse dynamics are presented in the part II-paper.

Generalized evolutes of planar curves

The evolutes of regular curves in the Euclidean plane are given by the caustics of regular curves. In this paper, we define the generalized evolutes of planar curves which are spatial curves, and the projection of generalized evolutes along a fixed direction are the evolutes. We also prove that the generalized evolutes are the locus of centers of slant circles of the curvature of planar curves. Moreover, we define the generalized parallels of planar curves and show that the singular points of generalized parallels sweep out the generalized evolute. In general, we cannot define the generalized evolutes at the singular points of planar curves, but we can define the generalized evolutes of fronts by using moving frames along fronts and curvatures of the Legendre immersion. Then we study the behaviors of generalized evolutes at the singular points of fronts. Finally, we give some examples to show the generalized evolutes.

Rigidity theorems for holomorphic curves in a complex Grassmann manifold G(3,6)

In this paper, we prove some local rigidity theorems of holomorphic curves in a complex Grassmann manifold [Formula: see text] by moving frames. By applying our rigidity theorems, we also give a characterization of all homogeneous holomorphic two-spheres in [Formula: see text] classified by the second author.

Neural Network-based Pest Detection with K-Means Segmentation: Impact of Improved Dragonfly Algorithm

Pest detection and identification of diseases in agricultural crops is essential to ensure good product since it is the major challenge in the field of agriculture. Therefore, effective measures should be taken to fight the infestation to minimise the use of pesticides. The techniques of image analysis are extensively applied to agricultural science that provides maximum protection to crops. This might obviously lead to better crop management and production. However, automatic pest detection with machine learning technology is still in the infant stage. Hence, the video processing-based pest detection framework is constructed in this work by following six major phases, viz. (a) Video Frame Acquisition, (b) Pre-processing, (c) Object Tracking, (d) Foreground and Background Segmentation, (e) Feature Extraction, and (f) Classification. Initially, the moving frames of videos are pre-processed, and the movement of the object is tracked with the aid of the foreground and background segmentation approach via K-Means clustering. From the segmented image, a new feature evaluation termed as Distributed Intensity-based LBP features (DI-LBP) along with edges and colour are extracted. Further, the features are subjected to a classification process, where an optimised Neural Network (NN) is used. As a novelty, the training of NN will be carried out using a new Dragonfly with New Levy Update (D-NU) algorithm via updating the weight. Finally, the performance of the proposed model is analysed over other conventional models with respect to certain performance measures for both video and image datasets.

Paradoxical stabilization of relative position in moving frames

To capture where things are and what they are doing, the visual system may extract the position and motion of each object relative to its surrounding frame of reference [K. Duncker, Routledge and Kegan Paul, London 161–172 (1929) and G. Johansson, Acta Psychol (Amst.) 7, 25–79 (1950)]. Here we report a particularly powerful example where a paradoxical stabilization is produced by a moving frame. We first take a frame that moves left and right and we flash its right edge before, and its left edge after, the frame’s motion. For all frame displacements tested, the two edges are perceived as stabilized, with the left edge on the left and right edge on the right, separated by the frame’s width as if the frame were not moving. This stabilization is paradoxical because the motion of the frame itself remains visible, albeit much reduced. A second experiment demonstrated that unlike other motion-induced position shifts (e.g., flash lag, flash grab, flash drag, or Fröhlich), the illusory shift here is independent of speed and is set instead by the distance of the frame’s travel. In this experiment, two probes are flashed inside the frame at the same physical location before and after the frame moves. Despite being physically superimposed, the probes are perceived widely separated, again as if they were seen in the frame’s coordinates and the frame were stationary. This paradoxical stabilization suggests a link to visual stability across eye movements where the displacement of the entire visual scene may act as a frame to stabilize the perception of relative locations.

Module to Support Real-Time Microscopic Imaging of Living Organisms on Ground-Based Microgravity Analogs

Since opportunities for spaceflight experiments are scarce, ground-based microgravity simulation devices (MSDs) offer accessible and economical alternatives for gravitational biology studies. Among the MSDs, the random positioning machine (RPM) provides simulated microgravity conditions on the ground by randomizing rotating biological samples in two axes to distribute the Earth’s gravity vector in all directions over time. Real-time microscopy and image acquisition during microgravity simulation are of particular interest to enable the study of how basic cell functions, such as division, migration, and proliferation, progress under altered gravity conditions. However, these capabilities have been difficult to implement due to the constantly moving frames of the RPM as well as mechanical noise. Therefore, we developed an image acquisition module that can be mounted on an RPM to capture live images over time while the specimen is in the simulated microgravity (SMG) environment. This module integrates a digital microscope with a magnification range of 20× to 700×, a high-speed data transmission adaptor for the wireless streaming of time-lapse images, and a backlight illuminator to view the sample under brightfield and darkfield modes. With this module, we successfully demonstrated the real-time imaging of human cells cultured on an RPM in brightfield, lasting up to 80 h, and also visualized them in green fluorescent channel. This module was successful in monitoring cell morphology and in quantifying the rate of cell division, cell migration, and wound healing in SMG. It can be easily modified to study the response of other biological specimens to SMG.

Invariants of Surfaces in Three-Dimensional Affine Geometry

Using the method of moving frames we analyze the algebra of differential invariants for surfaces in three-dimensional affine geometry. For elliptic, hyperbolic, and parabolic points, we show that if the algebra of differential invariants is non-trivial, then it is generically generated by a single invariant.