SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79, 1.45, 1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.

Ground-state properties of an artificial molecule: a simple model calculation for the double quantum dot

The ground state of a double quantum-dot structure is studied by a simplified Anderson-type model. Numerical calculations reveal that the ground-state level of this artificial molecule increases with the increasing single particle level of the dot, and also increases with the decreasing transfer integrals. We show the staircase feature of the electron occupation and the properties of the ground-state eigenvector by varying the;single particle level of the dot.

SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79 . 1.45 . 1.18-competitive separately for traditional timeout PM . adaptive predictive PM and stochastic PM.

Subretinal implantable artificial photoreceptor

The present study reports a subretinal implant device which can imitate the function of photoreceptor cells. Photodiode (PD) arrays on the chip translate the incident light into current according to the intensity of light. With an electrode at the end of every photodiode, the PDs transfer the current to the remnant healthy visual cells such as bipolar cells and horizontal cells and then activate these cells. Biocompatible character of the materials and artificial photoreceptor itself were tested and the photoelectric characteristics of the chips in simulative condition were described and discussed.

Kinetic Model Study on Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks

Enzymatic hydrolysis of cellulose was highly complex because of the unclear enzymatic mechanism and many factors that affect the heterogeneous system. Therefore, it is difficult to build a theoretical model to study cellulose hydrolysis by cellulase. Artificial neural network (ANN) was used to simulate and predict this enzymatic reaction and compared with the response surface model (RSM). The independent variables were cellulase amount X-1, substrate concentration X-2, and reaction time X-3, and the response variables were reducing sugar concentration Y-1 and transformation rate of the raw material Y-2. The experimental results showed that ANN was much more suitable for studying the kinetics of the enzymatic hydrolysis than RSM. During the simulation process, relative errors produced by the ANN model were apparently smaller than that by RSM except one and the central experimental points. During the prediction process, values produced by the ANN model were much closer to the experimental values than that produced by RSM. These showed that ANN is a persuasive tool that can be used for studying the kinetics of cellulose hydrolysis catalyzed by cellulase.

Quantum computation using artificial molecules

Two kinds of quantum computation systems using artificial molecules: quantum computer and quantum analog computer are described. The artificial molecule consists of two or three coupled quantum dots stacked along z direction and one single electron, In quantum computer, one-qubit and two-qubit gates are constructed by one molecule and two molecules, respectively. The coupling between two qubits in a quantum gate can be controlled by thin film electrodes. We also constructed a quantum analog computer by designing a three-dot molecule network and mapping a graph 3-colorability problem onto the network. The ground-state configuration of the single electrons in the network corresponds to one of the problem solutions, We numerically study the operations of the two kinds of the quantum computers and demonstrate that they quantum gates can perform the quantum computation and solve complex problems.

Artificial neural networks expecting more advanced microelectronics technology

　

Simultaneous determination of methylene blue and new methylene blue by slab optical waveguide spectroscopy and artificial neural networks

A slab optical waveguide (SOWG) has been used for study of adsorption of both methylene blue (MB) and new methylene blue (NMB) in liquid-solid interface. Adsorption characteristics of MB and NMB on both bare SOWG and silanized SOWG by octadecyltrichlorosilane (ODS) were compared. The simultaneous determinations of both MB and NMB were explored by flow injection SOWG spectrophotometric analysis and artificial neural networks (ANNs) for the first time. Concentrations of MB and NMB were estimated simultaneously with the ANNs. Results obtained with SOWG were compared with those got by conventional UV-visible spectrophotometry. (C) 2003 Elsevier Science B.V All rights reserved.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

Hemoglobin conjugated micelles based on triblock biodegradable polymers as artificial oxygen carriers

An artificial oxygen carrier is constructed by conjugating hemoglobin molecules to biodegradable micelles. Firstly a series of triblock copolymers (PEG-PMPC-PLA) in which the middle block contains pendant propargyl groups were synthesized and characterized. After the amphiphilic copolymer was self-assembled into core-shell micelles in aqueous solution, azidized hemoglobin molecules protected by carbon monoxide (CO) were conjugated to the micelles via click reaction between the propargyl and azido groups. The conjugation causes an increase of the micelle's mean diameter. Maximum conjugation ratio is 250 wt% in the hemoglobin-conjugated micelles (HCMs). Oxygen-binding ability of the HCMs was demonstrated by converting the CO-binding state of the HCMs into O-2-binding state.