Table Look-up Scheme Research Articles

Composite Quantization.

This paper studies the compact coding approach to approximate nearest neighbor search. We introduce a composite quantization framework. It uses the composition of several ($M$M) elements, each of which is selected from a different dictionary, to accurately approximate a $D$D-dimensional vector, thus yielding accurate search, and represents the data vector by a short code composed of the indices of the selected elements in the corresponding dictionaries. Our key contribution lies in introducing a near-orthogonality constraint, which makes the search efficiency is guaranteed as the cost of the distance computation is reduced to $O(M)$O(M) from $O(D)$O(D) through a distance table lookup scheme. The resulting approach is called near-orthogonal composite quantization. We theoretically justify the equivalence between near-orthogonal composite quantization and minimizing an upper bound of a function formed by jointly considering the quantization error and the search cost according to a generalized triangle inequality. We empirically show the efficacy of the proposed approach over several benchmark datasets. In addition, we demonstrate the superior performances in other three applications: combination with inverted multi-index, inner-product similarity search, and query compression for mobile search.

A general framework for shiftable position-based dual-image reversible data hiding

This paper proposes an improved method for shiftable position-based dual-image reversible data hiding. During the procedure of embedding data, the total number of shiftable coordinates is set as the parameter to make a trade-off between distortion and embedding rate. First, the optimal parameter is sought and the one-to-one code table is generated according to the expected embedding rate. Then, the message data is embedded into the cover image through a simple and effective code table lookup scheme to generate two visually similar stego images. During the procedure of data extraction, the message can be extracted in a reverse manner. The experimental results demonstrate that the proposed method has higher PSNR (peak signal-to-noise ratio) values than our previous work in the case of high embedding rates.

A novel table look-up scheme based on GFScom and its application

This work considers Mamdani fuzzy systems constructed from finite input–output data pairs using the generalized fuzzy sets with contradictory, opposite and medium negation (GFScom). Considering that the information available often consists of a set of finite numerical data pairs, a new table look-up scheme for constructing Mamdani fuzzy systems is presented. The designed fuzzy system is proved to be capable of approximating any real continuous function on a compact set to arbitrary degree of accuracy. We use this fuzzy modeling method for the truck back-upper control problem. The effectiveness of the proposed method is demonstrated by a comparison with the traditional table look-up scheme.

Fuzzy Lane-Changing Models with Socio-Demographics and Vehicle-to- Infrastructure System Based on a Simulator Test

Objective: We investigated the impacts of drivers’ socio-demographic factors on these lane-changing models, and developed Fuzzy logic-based lane-changing models associated the drivers’ demographic factors. Methods: Forty drivers were recruited for a driving simulator test to collect their driving behaviors of changing lane in a work zone with/without the aid of Driver’s Smart Advisory System (DSAS), a Vehicle-to-Infrastructure (V2I) communication system. Fuzzy table look-up scheme was selected to model Lane-Changing Reaction Time (LCRT) and Lane-Changing Reaction Distance (LCRD) incorporated with drivers' socio-demographic information and the collected driving behaviors. Findings: Without DSAS messages, the elders slowly responded to a static traffic guide of lane-change, but they didn’t change lane at the last minute. The highly educated and young drivers changed lane the earliest. When DSAS messages were provided, all drivers’ LCRT became shorter while their LCRD longer. Conclusion: Drivers’ age and the level of education are essential socio-demographic factors in lane-changing process. The DSAS is able to instruct all drivers to take earlier lane-changing actions. The developed models can predict drivers’ LCRT and LCRD accurately.

A fuzzy-based gas turbine fault detection and identification system for full and part-load performance deterioration

Considering the fact that the signs of performance deterioration in the main components of gas turbines vary in different operating points, thus designing a Fault Detection and Identification (FDI) system using a single fault pattern for all engine operating ranges can reduce the ability of the diagnosis system to identify the intensity or even the type of any potential degradation. So, in this article, with the aid of the “load” parameter as an augmented input and using the fault patterns obtained at different part-load conditions, a fuzzy-based FDI system is proposed for an industrial two-shaft gas turbine with the ability to use in both the full and part-load operations. In the proposed FDI system, fuzzy rule base is generated by a table look-up scheme and by employing the available input/output data extracted from fault signature table. Moreover, a global optimization technique is used to determine some database parameters, such as the number of membership functions of input variables and their standard deviations. The optimization is carried out to make a compromise between the diagnosis accuracy and robustness against measurement noise. In the present work, the performance of the proposed FDI system is evaluated against the most common cause of gas turbine performance deterioration i.e. fouling and erosion in terms of the success rate and the estimation accuracy at different levels of sensor noise. The results obtained indicate that the proposed FDI system can considerably reduce the average estimation error by 0.1–0.75% and increase the success rate by 10–20% compared with the diagnosis systems designed for a specific operating point. The results also demonstrate that the proposed FDI system has robust performance against measurement uncertainties, and moreover smearing effects are rarely seen in the results.

MODELING DATA FUZZY TIME SERIES WITH USING THE DECOMPOSITION OF SINGULAR AND VALUE THE APPLICATION OF INFLATION INFLUENCE LEVEL IN INDONESIA

The aims of this research are to construct a new method for modeling fuzzy time series data and to apply the method for forecasting Indonesian inflation rate. The procedure of this research is done by the following steps: (1) determine fuzzy relations using table lookup scheme, (2) Apply the singular value decomposition to reduce the unimportant fuzzy relations, (3) apply the method to forecasting Indonesian inflation rate. The result of this research is that it was designed a new method to construct the fuzzy time series model using singular value decomposition method. Then, the method is applied to forecast the Indonesian inflation rate based on fuzzy time series data. Forecasting inflation rate using the proposed method yields a higher accuracy than that using table lookup scheme and neural network methods.

Penerapan Logika Fuzzy Dalam Pemodelan Perkiraan Tingkat Inflasi Di Indonesia

Modeling for inflation rate in Indonesia was done by some researcher with conventional model. The conventional model can not be applied if the data are linguistic variables. Fuzzy system can be used to overcome the weak of this method. The goal of this research is to establish the model for forecasting inflation rate in Indonesia based on fuzzy time series data. In this research, forecasting inflation rate use table look up scheme method based on multivariate fuzzy time series data. This research is done by the following steps: 1) determine input-output data; 2) determine fuzzification for input-output data; 3) determine fuzzy rules base by table lookup scheme method; 4) construct fuzzy inference engine; 5) construct defuzzification; 6) construct fuzzy system for modeling inflation rate in Indonesia; 7) determine validation for model used MSE criteria. The results of this research are 1) we develop 8 fuzzy models; 2) The model with Gaussian membership function, minimum inference engine, 25 fuzzy rules has minimum MSE value, 5.5671. Therefore this model can be used to predict inflation rate in Indonesia based on inflation previously , credit interest rate, money supply, gross national product and exchange rate of Rupiah.

Developing Pedotransfer Functions for Estimating Field Capacity and Permanent Wilting Point Using Fuzzy Table Look-up Scheme

Study of soil properties like field capacity (F.C) and permanent wilting point (P.W.P) plays important roles in study of soil moisture retention curve. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. In this study, a new approach is proposed as a modification to a standard fuzzy modeling method based on the table look-up scheme. 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. Then, fuzzy table look-up scheme was employed to develop pedotransfer functions for predicting F.C and P.W.P using easily measurable characteristics of clay, silt, O.C, S.P, B.D and CaCO3. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The value of RMSE and R2 derived by fuzzy table look-up scheme for F.C and P.W.P were (1.65, 0.87) and (1.03, 0.83), respectively.

Modeling of Soil Cation Exchange Capacity Based on Fuzzy Table Look-up Scheme and Artificial Neural Network Approach

In this study, a new approach is proposed as a modification to a standard fuzzy modeling method based on the table look-up scheme. 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. Then, neural network model (feed-forward back propagation network) and fuzzy table look-up scheme were employed to develop a pedotransfer function for predicting soil CEC using easily measurable characteristics of clay and organic carbon. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The value of RMSE and R2 derived by ANN model for CEC were 0.47 and 0.94 respectively, while these parameters for fuzzy table look-up scheme were 0.33 and 0.98 respectively. Results showed that fuzzy table look-up scheme had better performance in predicting and modeling of soil cation exchange capacity than artificial neural network.

Research on Control Strategy of Maximum Power Point Tracking of VSCF Wind Generation

Variable speed constant frequency wind turbines can get the maximum power by adjusting the rotor speed of the induction generator according to the variable wind speed. The mathematical relationship between wind speed of wind generation and power of wind turbine power is studied in this paper, then control strategy of maximum power point tracking consists of comparative law of the greatest power, table look-up scheme of the best tip speed ratio and a variable step mountain climb algorithm and the like., and advantages and drawbacks of them are compared. Finally a variable step mountain climb algorithm for an induction generator is studied and the wind turbine model is tested by MATLAB simulation. System simulation results have confirmed the functionality and performance of this method.

Small Rotor Design Optimization Using Blade Element Momentum Theory and Hover Tests

The current investigation focuses on the development of a low-cost computational methodology to design and optimize hovering rotors for small-scale vehicles using circular arc airfoils. Rotors for vehicles having a representative Re between 5000 and 60,000 were considered. A detailed experimental study of rectangular and tapered blades generated the data necessary to identify main performance trends and the effect of planform modifications. A blade element momentum theory model coupled with a table lookup scheme was implemented. The database was interpolated along three dimensions (Re, camber, and angle of attack) to obtain the local aerodynamic coefficients used in the calculation of the nonlinear inflow distribution along the blade span. Two methodologies were used in the calculation of the database. First, a purely numerical approach using the two-dimensional flow solver INS2D was evaluated. Second, a reverse method that used the experimental rotor data to refine the original previously generated database was investigated. Validation showed that the model predictive capabilities improved with the empirical corrections. An optimization algorithm was implemented to perform a grid search using power loading as the hover efficiency metric. The methodology proposed is able to optimize blade geometry and operating conditions following imposed constraints within a defined design space.

Congestion-aware topology optimization of structured power/ground networks

This paper presents an efficient method for optimizing the design of power/ground (P/G) networks by using locally regular, globally irregular grids. The procedure divides the power grid chip area into rectangular subgrids or tiles. Treating the entire power grid to be composed of many tiles connected to each other enables the use of a hierarchical circuit analysis approach to identify the tiles containing the nodes having the greatest drops. Starting from an initial configuration with an equal number of wires in each of the rectangular tiles, wires are added in the tiles using an iterative sensitivity based optimizer. A novel and efficient table lookup scheme is employed to provide gradient information to the optimizer. Incorporating a congestion penalty term in the cost function ensures that regularity in the grid structure does not aggravate congestion. Experimental results on test circuits of practical chip sizes show that the proposed P/G network topology, after optimization, saves 12%-23% of the chip-wiring area over other commonly used topologies.

Efficient IP forwarding engine with incremental update

Nowadays, the commonly used table lookup scheme for IP routing is based on the so-called classless interdomain routing (CIDR). With CIDR, routers must find out the best matching prefix (BMP) for IP...

An online learning fuzzy controller

A fuzzy controller with online learning capability is reported in this paper. The controller learns from a standard proportional plus derivative (PD) controller. It is implicitly assumed that the tuning parameters of the PD controller are already known. The learning is realized via Wang's table lookup scheme. The controllers are applied successfully to control an open-loop unstable system, i.e., the ball and plate system. Experimental studies have demonstrated the performance of the proposed controller.

Chip design of MFCC extraction for speech recognition

The mel frequency cepstral coefficient (MFCC) is one of the most important features required among various kinds of speech applications. In this paper, the first chip for speech features extraction based on MFCC algorithm is proposed. The chip is implemented as an intellectual property, which is suitable to be adopted in a speech recognition system on a chip. The computational complexity and memory requirement of MFCC algorithm are analyzed in detail and improved greatly. The hybrid table look-up scheme is presented to deal with the elementary function value in the MFCC algorithm. Fixed-point arithmetic is adopted to reduce the cost under the accuracy studies of finite word length effect. Finally, the area-efficient design is implemented successfully into the single Xilinx XC4062XL FPGA.

Antecedent validity adaptation principle for fuzzy systems tuning

This paper proposes an antecedent validity adaptation (AVA) principle for fuzzy systems tuning. It is suggested that the fuzzy rules may be updated with respect to the validity of antecedents. This adaptation principle agrees with the human intuition and fuzzy logic reasoning. The principle is first applied to table look-up (TL) scheme to model recorded data; an on-line fuzzy identification algorithm is also proposed. Next, the principle is applied to fuzzy clustering algorithm. These methods are successfully applied to model nonlinear systems.

High-performance IP routing table lookup

Nowadays, the commonly used table lookup scheme for IP routing is based on the so-called classless interdomain routing (CIDR). With CIDR, routers must find out the best matching prefix (BMP) for IP packets forwarding, which complicates the IP lookup. Currently, this process is mainly done in software and several schemes have been proposed for hardware implementation. Since the IP lookup performance is a major design issue for the new generation routers, in this article we propose a fast IP address lookup scheme, which significantly reduces the forwarding table to fit into SRAM with very low cost. It can also be implemented in hardware using the pipeline technique. By using our proposed method, the required memory space can be 90–170 Kb less than the previous scheme and three memory accesses for a lookup in the worst case. When implemented in hardware pipeline architecture, our mechanism can achieve one routing lookup per memory access.

An efficient IP routing lookup by using routing interval

Nowadays, the commonly used table lookup scheme for IP routing is based on the so-called classless interdomain routing (CIDR). With CIDR, routers must find out the best matching prefix (BMP) for IP packets forwarding, this complicates the IP lookup. Currently, this process is mainly performed in software and several schemes have been proposed for hardware implementation. Since the IP lookup performance is a major design issue for the new generation routers, in this article we investigate the properties of the routing table and present a new approach for IP lookups, our approach is not based on BMP and significantly reduces the complexity, thus the computation cost of existing schemes can be significantly reduced. We also propose an efficient IP lookup algorithm, with which we improve the binary search on prefixes to 30 millions of packets per second (MPPS) and 5,000 route updates/s under the same experiment setup with an even larger routing table.

Adaptive Compensation of Nonlinearity in High Power Amplifier by Support Vector Machine

This paper is concerned with a new adaptive predistortion method for compensation of linear and nonlinear distortions of a high power amplifier (HPA), which is frequently used in the orthogonal frequency division multiplexing (OFDM) communication scheme and mobile communication systems. The proposed nonlinear method is based on the support vector machine (SVM), which can construct an inverse system of the nonlinear amplifier through learning using a given number of training samples. An on-line adaptive algorithm for updating the parameters of SVM is also investigated to reduce its computational burden of the batch-processed optimization. By comparing with a case without predistortion and ordinary table-lookup scheme, it is shown that the proposed data-based approach can improve the suppression of the power spectrum of the amplifier output outside the input frequency band caused by the nonlinear distortions.

The fine structures in I–V curves of single-electron transistor made by double unit cells of Bi 2Sr 2CaCu 2O 8

Layered structures of copper oxide HTSC may provide ultra-small capacitance and large enough resistance for single-electron tunneling between strongly superconducting layers. The single-electron transistor made by double unit cells of Bi 2Sr 2CaCu 2O 8 can be calculated on the basis of semi-classical theory for single-electron tunneling and a method developed specifically to describe the behaviors of HTSC. To achieve satisfactory simulation speed, a table-lookup scheme is proposed. The simulation results, which display fine structures, are presented. This simulation scheme can also be extended to simulate other single-electron devices made by unit cells of HTSC and single-electron circuits consisting of these devices.