Review of some advances and applications in real-time high-speed vision: Our views and experiences

The frame rate of conventional vision systems is restricted to the video signal formats(e.g., NTSC 30 fps and PAL 25 fps that are designed on the basis of the characteristics of the human eye, which implies that the processing speed of these systems i limited to the recognition speed of the human eye. However, there is a strong demand for real-time high-speed vision sensors in many application fields, such as factory automation, biomedicine, and robotics, where high-speed operations are carried out. Thes high-speed operations can be tracked and inspected by using high-speed vision systems with intelligent sensors that work at hundred of Hertz or more, especially when the operation is difficult to observe with the human eye. This paper reviews advances in developing real-time high speed vision systems and their applications in various fields, such as intelligent logging systems, vibration dynamic sensing, vision-based mechanical control, three-dimensional measurement/automated visual inspection, vision-based human interface and biomedical applications.     

Study on stiffness visualization and safety control based on will-consensus building for tele-palpation robot system

This paper proposes a method for visualizing the stiffness of a soft object in a palpation-support information system by the teleoperation of a robot hand. It is important that a palpation system display a body’s shape and stiffness. In our method, the stiffness of the contact area between the soft object and the robot finger is estimated by a recursive least-squares method with forgetting factor that uses an impedance dynamics model. With the estimated stiffness and direction of contact force, we calculate the scalar parameter for visualization of stiffness. Moreover, we propose a safety control method for the palpation system, which is part of a tele-control method based on will-consensus building. The system configuration, estimated algorithm, and experimental results are presented.     

Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments

We extend an abstract agent-based swarming model based on the evolution of neural network controllers, to explore further the emergence of swarming. Our model is grounded in the ecological situation, in which agents can access some information from the environment about the resource location, but through a noisy channel. Swarming critically improves the efficiency of group foraging, by allowing agents to reach resource areas much more easily by correcting individual mistakes in group dynamics. As high levels of noise may make the emergence of collective behavior depend on a critical mass of agents, it is crucial to reach sufficient computing power to allow for the evolution of the whole set of dynamics in simulation. Since simulating neural controllers and information exchanges between agents are computationally intensive, to scale up simulations to model critical masses of individuals, the implementation requires careful optimization. We apply techniques from astrophysics known as treecodes to compute the signal propagation, and efficiently parallelize for multi-core architectures. Our results open up future research on signal-based emergent collective behavior as a valid collective strategy for uninformed search over a domain space.     

Gain‐Scheduled Control via Switching of LTI Controllers and State Reset

This paper proposes a synthesis method of gain‐scheduled control systems that switch linear time‐invariant controllers according to hysteresis of the scheduling parameter. Stability and L₂‐gain analysis and synthesis methods for switched systems are applied to the switched gain‐scheduled control synthesis using reset of the controller state, where also the reset law is computed via linear matrix inequalities (LMIs). In addition to optimization of an upper bound of L₂‐gain, we reduce jumps of control input via an auxiliary optimization. Numerical examples are presented to illustrate the switched gain‐scheduled controller.     

Microscopic image restoration based on tensor factorization of rotated patches

In microscopic image processing for analyzing biological objects, structural characters of objects such as symmetry and orientation can be used as a prior knowledge to improve the results. In this study, we incorporated filamentous local structures of neurons into a statistical model of image patches and then devised an image processing method based on tensor factorization with image patch rotation. Tensor factorization enabled us to incorporate correlation structure between neighboring pixels, and patch rotation helped us obtain image bases that well reproduce filamentous structures of neurons. We applied the proposed model to a microscopic image and found significant improvement in image restoration performance over existing methods, even with smaller number of bases.     

control with limited communication and message losses

We propose an H^∞ approach to a remote control problem where the communication is constrained due to the use of a shared channel. The controller employs a periodic time sequencing scheme for message transmissions from multiple sensors and to multiple actuators of the system. It further takes into account the information on the random message losses that occur in the channel. An exact characterization for controller synthesis is obtained and is stated in terms of linear matrix inequalities. Furthermore, an analysis on the loss probabilities of the messages to accomplish stabilization is carried out. The results are illustrated through a numerical example.     

Event-triggered control for discrete-time multi-agent average consensus

We consider the average consensus problem for the multi-agent system in the discrete-time domain. Three triggering based control protocols are developed, which dictate the broadcast and control update instants of individual agents to alleviate communication and computational burden. Lyapunov-based design methods prescribe when agents should communicate and update their control so that the network converges to the average of agents' initial states. We start with a static version of the distributed event-triggering law and then generalize it so that it involves an internal auxiliary variable to regulate the threshold dynamically for each agent. The third protocol uses a self-triggering algorithm to avoid continuous listening wherein each agent estimates its next triggering time and broadcasts it to its neighbors at the current triggering time. Numerical simulations are shown to validate the efficacy of the proposed algorithms.     

Market prediction using machine learning based on social media specific features

Artificial Life and Robotics - In recent years, unspecified messages posted on social media have significantly affected the price fluctuations of online-traded products, such as stocks and virtual...     

Bounded Rationality,Group Formation and the Emergence of Trust: An Agent-Based Economic Model

Computational Economics - It is possible to model trust as an investment game, where a player in order to receive a reward or a better outcome, accepts a certain risk of defection by another...     

Revolving Vernier Mechanism Controls Size of Linear Homomultimer

A new kind of the Vernier mechanism that is able to control the size of linear assembly of DNA origami nanostructures is proposed. The mechanism is realized by mechanical design of DNA origami, which consists of a hollow cylinder and a rotatable shaft in it connected through the same scaffold. This nanostructure stacks with each other by the shape complementarity at its top and bottom surfaces of the cylinder, while the number of stacking is limited by twisting angle of the shaft. Experiments have shown that the size distribution of multimeric assembly of the origami depends on the twisting angle of the shaft; the average lengths of the multimer are decamer, hexamer, and tetramer for 0°, 10°, and 20° twist, respectively. In summary, it is possible to affect the number of polymerization by adjusting the precise shape and movability of a molecular structure.