The G4-FET, a four-gate transistor compatible with standard silicon-on-insulator (SOI) CMOS technology, provides unique opportunities as a logic device. Combining both JFET- and MOSFET-like actions within one transistor body, the G4-FET offers two side (lateral) junction-based gates, a top MOS gate, and a MOS back gate that is activated by SOI substrate biasing. The G4-FET's conduction characteristics are controlled by the combined interaction of these four gates. In this paper, the G4-FET is demonstrated as a logic device, resulting in a universal and programmable logic gate that can lead to the design of more efficient logic circuits. As an example, we present a new full adder design based on the G4-FET that is significantly more efficient than conventional designs.

Abstract The paper presents the implementation of a new class of massively parallel algorithms for solving certain time‐dependent partial differential equations (PDEs) on massively parallel supercomputers. Such PDEs are usually solved numerically, by discretization in time and space, and by applying a time‐stepping procedure to data and algorithms potentially parallelized in the spatial domain. In a radical departure from such a strictly sequential temporal paradigm, we have developed a concept of time‐parallel algorithms, which allows the marching in time to be fully parallelized. This is achieved by using a set of transformations based on eigenvalue‐eigenvector decomposition of the matrices involved in the discrete formalism. Our time‐parallel algorithms possess a highly decoupled structure, and can therefore be efficiently implemented on emerging, massively parallel, high‐performance supercomputers, with a minimum of communication and synchronization overhead. We have successfully carried out a proof‐of‐concept demonstration of the basic ideas using a two‐dimensional heat equation example implemented on the Intel Touchstone Delta supercomputer. Our results indicate that linear, and even superlinear, speed‐up can be achieved and maintained for a very large number of processor nodes.

Many space robotic systems would be required to operate in uncertain or even unknown environments. The problem of identifying such environment for compliance control is considered. In particular, neural networks are used for identifying environments that a robot establishes contact with. Both function approximation and parameter identification (with fixed nonlinear structure and unknown parameters) results are presented. The environment model structure considered is relevant to two space applications: cooperative execution of tasks by robots and astronauts, and sample acquisition during planetary exploration. Compliant motion experiments have been performed with a robotic arm, placed in contact with a single-degree-of-freedom electromechanical environment. In the experiments, desired contact forces are computed using a neural network, given a desired motion trajectory. Results of the control experiments performed on robot hardware are described and discussed.>

A type of dynamical system for solving optimization problems is introduced. The approach exploits a novel paradigm in nonlinear dynamics that is based upon the concept of terminal attractors and repellers. A class of dynamical systems-the unpredictable systems-is introduced and analyzed. These systems are represented in the form of coupled activation and learning dynamical equations whose ability to be spontaneously activated is based upon two pathological characteristics: (1) Such systems have zero Jacobian. As a result, they have an infinite number of equilibrium points which occupy curves, surfaces, or hypersurfaces. (2) At all of these equilibrium points, the Lipschitz condition fails, so the equilibrium points become terminal attractors or repellers, depending upon the sign of the periodic excitation. These characteristics result in multichoice response and lead to unpredictable dynamical systems. The systems can be controlled by sign strings which uniquely define the systems' behavior by specifying the direction of the motions at the critical points. By changing the combinations of signs in the code strings, a system can reproduce any prescribed behavior to a prescribed accuracy, which is why the unpredictable systems driven by sign strings are extremely flexible and can be exploited for solving optimization problems