The functional reanimation of paralyzed limbs.

The functional reanimation of paralyzed limbs has been a longstanding goal of neural prosthetic research, but clinically successful applications have been elusive. Natural voluntary limb movement requires four major elements: actuators (i.e., motor units), sensors (i.e., somatosensory afferents), commands (i.e., cerebral cortical activity), and control (i.e., integration of the previous three elements at various levels of the neuraxis). Prosthetic equivalents of each of these elements are, as yet, primitive and often cumbersome to deploy, but new technologies promise substantial improvements for all. This article focuses on one such technology, bionic neuon (BION) modular microimplants, and its relationship to alternative and complementary technologies. The challenge remains to select and integrate them into systems that can be tailored efficiently to the widely disparate needs of patients with various patterns of weakness and paralysis.     

Empirical models for net-length probability distribution and applications

In this paper, we propose a novel, empirical, and parameterizable model for estimating the probability distribution of wire length for each net in a placed netlist. The model is simple and fast to compute. We did extensive experimentation with state-of-the-art commercial (Cadence) and academic (Parquet and Labyrinth) tools and validated our model. Our distribution model was around three times more accurate than assuming half-perimeter bounding box as the fixed net-length estimate. Since the model is parameterizable it can be easily tailored for different routing tools and benchmarks. This model would be very useful in defining a full fledged probabilistic design automation methodology in which various design metrics are optimized from a probabilistic point of view. We also discuss the application of our model in a novel probabilistic approach to the buffer insertion problem.     

Efficient designs of reversible sequential circuits

The Journal of Supercomputing - Recently, the synthesis of reversible sequential circuits has attracted researchers’ attention for implementing low-power logic designs. So far, the direct and...     

Extremal optimization vs. learning automata: Strategies for spin selection in portfolio selection problems

Nowadays, various imitations of natural processes are used to solve challenging optimization problems faster and more accurately. Spin glass based optimization, specifically, has shown strong local search capability and parallel processing. But, spin glasses have a low rate of convergence since they use Monte Carlo simulation techniques such as simulated annealing (SA). Here, we propose two algorithms that combine the long range effect in spin glasses with extremal optimization (EO-SA) and learning automata (LA-SA). Instead of arbitrarily flipping spins at each step, these two strategies aim to choose the next spin and selectively exploiting the optimization landscape. As shown in this paper, this selection strategy can lead to faster rate of convergence and improved performance. The resulting two algorithms are then used to solve portfolio selection problem that is a non-polynomial (NP) complete problem. Comparison of test results indicates that the two algorithms, while being very different in strategy, provide similar performance and reach comparable probability distributions for spin selection. Furthermore, experiments show there is no difference in speed of LA-SA or EO-SA for glasses with fewer spins, but EO-SA responds much better than LA-SA for large glasses. This is confirmed by tests results of five of the world's major stock markets. In the last, the convergence speed is compared to other heuristic methods such as Neural Network (NN), Tabu Search (TS), and Genetic Algorithm (GA) to approve the truthfulness of proposed methods.     

On problem transformability in VLSI

The two basic performance parameters that capture the complexity of any VLSI chip are the area of the chip,A, and the computation time,T. A systematic approach for establishing lower bounds onA is presented. This approach relatesA to the bisection flow, . A theory of problem transformation based on , which captures bothAT ² andA complexity, is developed. A fundamental problem, namely, element uniqueness, is chosen as a computational prototype. It is shown under general input/output protocol assumptions that any chip that decides ifn elements (each with (1+)lognbits) are unique must have =(nlogn), and thus, AT²=(n ²log² n), andA= (nlogn). A theory of VLSI transformability reveals the inherentAT ² andA complexity of a large class of related problems.This work was supported in part by the Semiconductor Research Corporation under contract RSCH 84-06-049-6.     

Anionic synthesis of poly(epichlorohydrin-g-styrene) and chemical modification of polyepichlorohydrin

Poly(epichlorohydrin-g-styrene) copolymers, P(ECH-g-S)s, have been prepared in solution by two different methods of coupling polyepichlorohydrin, PECH, with polystyrene, PS, containing a functional end group. In the first method, polystyryl carboxylic acid, PSCOOH, prepared from living polystyrene, was converted to the corresponding potassium salt which was coupled with PECH in dimethyl formamide solution usually at 60°C. In the second method, polystyryl potassium, PS^?K⁺, in tetrahydrofuran solution was end-capped with propylene sulphide to form PSCH₂CH(CH₃)S^?K⁺ which was coupled directly with PECH in tetrahydrofuran at 40°C. Grafting by the first reaction was relatively slow but occurred without significant adverse side reactions. The grafting by the second reaction was relatively fast, but side reactions occurred resulting in a cleavage of the graft copolymer. The graft copolymers were purified by fractional precipitation and extraction procedures and were characterized by infra-red and ultra-violet spectroscopy, microanalysis, gel permeation chromatography, differential scanning calorimetry, solution viscometry, dynamic mechanical testing, and electron microscopy. Solution properties of the graft copolymers in benzene-carbon tetrachloride and toluene-cyclohexane mixtures were studied. There was evidence of micelle formation in dilute solutions of the mixed solvents and microphase separation of components in the solid state. Nucleophilic substitution of chlorine atoms in PECH by phenyl acetate (C₆H₅ CH₂COO-) and thiophenoxide (C₆H₅S-) groups was performed.     

Implementing a one-bit reversible full adder using quantum-dot cellular automata

Quantum-dot cellular automata (QCA) technique is one of the emerging and promising nanotechnologies. It has considerable advantages versus CMOS technology in various aspects such as extremely low power dissipation, high operating frequency and small size. In this paper, designing of a one-bit full adder is investigated using a QCA implementation of Toffoli and Fredkin gates. Then, a full adder design with reversible QCA1 gates is proposed regarding to overhead and power savings. Our proposed full adder design is more preferable when considering both circuit area and speed. The proposed design uses only two QCA1 gates and maximizes the circuit density and focuses on a layout of the circuit which is minimal in using QCA cells.     

An H∞non‐fragile observer‐based adaptive sliding mode controller design for uncertain fractional‐order nonlinear systems with time delay and input nonlinearity

In this paper the problem of non‐fragile adaptive sliding mode observer design is addressed for a class of nonlinear fractional‐order time‐delay systems with uncertainties, external disturbance, exogenous noise, and input nonlinearity. An H_∞ observer‐based adaptive sliding mode control considering the non‐fragility of the observer is proposed for this system. The sufficient asymptotic stability conditions are derived in the form of linear matrix inequalities. It is proven that the sliding surface is reachable in finite time. An illustrative example is provided which corroborates the effectiveness of the theoretical results.     

Economic selection of process mean and lot‐sizing decisions for multi‐newsvendor supply chain

Two related problems are integrated in this paper, the first is the targeting problem and another is production/inventory decisions in a supply chain. The supply chain under consideration consists of a supplier of raw material, a single producer, and multiple newsvendors. The producer can adjust the process mean before starting the production run. Once set to a certain target value, the process mean is not changed until the production lot is completed. At the end of a production run, the producer screens the lot and uses specification limits to evaluate the quality of the item. Nonconforming items are scrapped with no salvage value; however, conforming items are delivered to newsvendors who are subjected to random demand. If demand on a newsvendor in a season is lower than anticipated, surplus items will be returned to the producer at a certain transportation cost. We first develop a mathematical model that maximizes the expected total profit of the supply chain. Then, a table for two special functions is devised to simplify the solution method and is used to find the optimal solution of the proposed model. We also study the significance of this integration by comparing the performance of the proposed model with an independent model where the process mean selection and lot‐sizing decisions are found separately.