Non-uniform Segmentation Research Articles

Characterizing longshore transport potential and divergence of drift to inform beach loss trends

Beach loss is a growing global challenge that threatens the safety of coastal communities, the health of coastal ecosystems, recreational amenities, and regional economies dependent on tourism. Spatial gradients in longshore sediment transport, or divergence of drift (DoD), is a primary driver of beach width change over multi-annual time scales, but the response of any particular beach can be challenging to characterize and predict. Here we present a new method to characterize DoD using a non-uniform segmentation of the coastline informed by the spatial distribution of longshore transport potential, including the location of physical barriers identified using satellite imagery, and both maxima and reversals in longshore transport potential derived from nearshore wave data. The method demonstrates improved capacity to predict beach width trends at sandy beaches compared to methods that rely on a uniform segmentation of the coastline. In an application to southern California where satellite data documents two decades of beach width change, the non-uniform segmentation method correctly predicts the sign of beach trends at 93% of transects within a littoral cell and achieves a linear correlation between DoD and beach width trends exceeding 0.8. Moreover, we find that a minimum of five years of data are required to establish consistently strong correlations between DoD and beach width changes. Conversely, use of a uniform segmentation is shown to be unreliable for estimating beach width trends due to strong sensitivities to shoreline segment size. This work shows the potential to leverage nearshore wave data and satellite-based beach width data for improved characterization of shoreline dynamics relevant to beach erosion management.

Equivalent Circuits of Dipole Antennas for Broadband Applications

In this paper distributed parameter equivalent circuits are developed for linear dipoles, dielectric coated dipoles and lumped loaded dipoles. Theoretical solutions for each distributed parameter and the reasonable non-uniform segmentation of antennas serve as the foundation for the derivation. The accomplished validations of the modeling procedures indicate that the given equivalent circuits are capable of correctly describing dipole antennas in frequency and time domains, with the advantages of wideband, frequency independence and unambiguous physical meaning. Relying on the presented equivalent circuits, broadband issues such as simulating input impedance, predicting equivalent lengths and computing transient responses of dipole antennas can be readily addressed. In addition, the circuit model provides helpful insights into the analysis and design for the loaded dipole antennas.

Accurate Capacitance Matrices for Multiconductor Transmission Lines

When analyzing multiconductor transmission lines with the edge coupling by the method of moments, the matrices of per-unit-length parameters may be inaccurate, which leads to an unacceptable noncausal response at the end of the transmission line. The article proposes a simple technique of a nonuniform segmentation of multiconductor transmission lines with edge coupling boundaries to eliminate the inaccurate calculation of the capacitance matrix. This technique allows obtaining accurate capacitance matrices with the lowest computational costs on single-variant and multivariate analyses, as well as optimization or stability analysis.

Periodic Nonlinear Error Analysis and Compensation of a Single-Excited Petal-Shaped Capacitive Encoder to Achieve High-Accuracy Measurement.

The measurement results of a single-excitation petal-shaped capacitive encoder show strong periodic characteristics for nonlinear errors. This paper presents the analysis of periodic nonlinear errors in a single-excitation petal-shaped encoder in terms of three main aspects—sensitive structure processing error, circuit demodulation error, and installation error. Analytical and simulation results confirm that the first-, second-, and fourth-periodic electrical errors are caused by the misalignment of circuit parameters, non-uniform segmentation of the processing error, and cross interference of the electric field, respectively. Further experimental investigation reveals that the mechanical periodic error is caused by installation misalignment. Based on these analytical, simulation, and experimental results, the design of the capacitive encoder was optimized and a method based on harmonic components was applied to compensate the periodic nonlinear error of the encoder. Measurement results shows that the prototype which has 180 petal-shaped numbers can achieve a reduction of periodic nonlinear errors to less than 0.02° and its accuracy can be improved to 0.0006° after compensation over the full measurement range.

Dual-Channel Multiplier for Piecewise-Polynomial Function Evaluation for Low-Power 3-D Graphics

A dual-channel multiplier (DCM) for energy efficient second-order piecewise-polynomial function evaluation for 3-D graphics applications is presented in this paper. The performance of the evaluation process is highly dependent on the design of the multiplication and squaring structure. A novel hardware implementation for polynomial evaluation is presented. The proposed approach compensates the complex multipliers by using DCM which reduces the hardware complexity. The DCM scheme performs complex functions with power-efficient and area-efficient approach. The multiplier reduces the hardware computational effort in the piecewise polynomial approximation with uniform or nonuniform segmentation. For large operand input size, a multiplier adder converter and a dedicated radix-4 squaring unit are also proposed. These units achieve the least power consumption compared to previous approaches with large input word size. Comparison with general purpose multiplication has shown reduction in power, and delay by up to 36%, and 50%, respectively. The proposed technique exhibits up to 93% saving in power consumption compared to the current traditional schemes.

Analysis of MAC Protocol for Reliable Broadcast

In wireless communication It is important to find a reliable broadcasting protocol that is especially designed for an optimum performance of public-safety and data travelling related applications. Using RSU and OBU, there are four novel ideas presented in this research work, namely choosing the nearest following node as the network probe node, headway-based segmentation, non-uniform segmentation and application adaptive. The integration of these ideas results in a protocol that possesses minimum latency, minimum probability of collision in the acknowledgment messages and unique robustness at different speeds and traffic volumes. Wireless communications are becoming the dominant form of transferring information,and the most active research field. In this dissertation, we will present one of the most applicable forms of Ad-Hoc networks; the Vehicular Ad-Hoc Networks (VANETs). VANET is the technology of building a robust Ad-Hoc network between mobile vehicles and each other, besides, between mobile vehicles and roadside units.

Stochastic Optimal Scheduling Based on Scenario Analysis for Wind Farms

In the scheduling of wind farms, the fluctuation of system's power demand is required to track within a certain time period. It benefits to reasonably arrange division of wind turbines and distribution of active power output. This paper proposes a stochastic optimal scheduling method based on scenario analysis for wind farms. While tracking the power demand on the system side, the uncertainty of wind speed and the effect of wake flow in the wind farm are also considered in the model in order to minimize the operating cost. The method of scenario analysis can determine uncertain variables, and the nonuniform segmentation piecewise linearization method is used to linearize the model. The mixed-integer linear programming method is used to figure out the optimal scheduling strategy for the wind farm, thus, greatly increasing the economical operation of the wind farm. This proposed method is proved to be effective on an actual wind farm in East Inner Mongolia of China.

Optimal Segmentation for Piecewise RF Power Amplifier Models

Accurate modeling of an RF power amplifier and/or its inverse is the core element of every digital predistortion system. An interesting alternative to the family of classic polynomial models are piecewise models, which divide the magnitude range into segments and define gain/phase-distortion through complex-valued functions on a per-segment basis. Naturally, the question arises whether a well-chosen non-uniform segmentation outperforms straightforward uniform segmentation and whether the benefit outweighs the extra effort. This work has two contributions: First, the segmentation that is optimal in the least-squares sense is determined jointly with the model coefficients and its benefit in terms of linearization improvement is demonstrated through measurements on a Doherty power amplifier. Second, a reduced-complexity approach with negligible performance loss is proposed.

Approximate computing of two-variable numeric functions using multiplier-less gradients

Approximate computing of non-trivial numeric functions is a well-known design technique and, therefore, used in several different application areas. Its main idea is the relaxation of conventional correctness constraints to achieve high performance results in terms of throughput and/or energy consumption. In this paper we propose an automated approximate design method for the fast hardware generation of two-dimensional numeric functions. By using multiplier-less gradients in combination with an advanced non-uniform segmentation scheme, high hardware performance is achieved. To qualify our approach, exhaustive evaluation is carried out, considering six different two-variable numeric functions. In a first step, a global complexity estimation is performed with varying design constraints on the algorithmic level. Out of this, most suitable candidates are selected for logic and physical CMOS synthesis. The results are compared to actual references highlighting our work as a powerful approach for the hardware-based calculation of two-variable numeric functions, especially in terms of throughput and energy consumption.

Modeling the Distributive Effects of RC Transmission Line Using Recursive Segmentation and Applications to MOSFETs and BJTs

A method for modeling the distributive effects of RC transmission line is developed which is applicable to cases in which one end or both ends are connected to the input signal. Nonuniform recursive segmentation is used. The segments are categorized into three types, with each modeled by a distinctive and asymmetric lumped circuit in $\pi $ topology. The number of segments is determined by the frequency range required. The new method is applied to model the distributed MOSFET gate and bipolar junction transistor base. Both frequency and transient response characteristics are much better modeled compared with existing methods.

Personal Identification Using Ears Based on Statistical Features

Biometrics is an automated method of recognizing a person based on a physiological (e.g. face, iris, or retina) or behavioral (e.g. gait, signature, or dynamic keystrokes) characteristics. Ear recognition is one of the physiological biometrics' types that have been interested in the recent years. Ear recognition, achieves good accuracy and has many advantages such as it doesn't affected by expressions, health, and more stable than many other biometrics. However, it has many challenges such as the pose of the face, lighting variation, occlusion with hair or clothes. In this research, four proposed models are used to identify people using ear images. The first model used single feature extraction method based on single classifier. While, the second model used single feature extraction method based on multi-classifiers. The third model used feature combination techniques (parallel or serial) based on single classifier. Finally, in the fourth model multi-features and multi-classifiers are used. In this research, there are four methods that are used to extract the features, namely, \textit{Principal Component Analysis} (PCA), \textit{Linear Discriminant Analysis} (LDA), \textit{Independent Component Analysis} (ICA), and \textit{Discrete Cousin Transform} (DCT). Neural networks, decision tree, and minimum distance classifiers are used to classify the unknown samples. The occlusion problem with hair or scarves is one of the big challenges of the ear recognition systems. In this research, segmentation technique is proposed to neglect the occluded part and solve the occlusion problem. The idea of the segmentation technique is based on dividing the ear images into different parts. The occluded part/s is neglected and the rest of the parts are used to identify people based on features fusion and classifiers fusion. The segmentation technique consists of two main types, namely, uniform or non-uniform segmentation techniques. In this research, the uniform segmentation technique is used for many experiments (horizontal, vertical, and grid). All the four proposed models are applied to all ear segments to investigate the power of each model and to achieve a high accuracy. In this research, ear database images is used. The ear dataset consists of 102 grayscale images (6 images for each of 17 subjects) in PGM format [1]. The proposed models are achieved good identification rates using ear images. In the first model, the best accuracy achieved using LDA and neural network classifier. The results of the first model ranged from 64.12\% to 100\%. In the second model, many classifiers are fused to increase the recognition rate. In this method, two methods are used, namely, Borda count and majority voting. The results of this model ranged from 94.12\% to 96.08\%. The third model, the features using two different methods, namely serial and parallel are combined. The results of this model prove that the serial combination is more powerful than parallel combination. Finally, in the fourth model, two features and two classifiers are fused to get one decision. The accuracy of this model is approximately the same of the third model, and it does not achieve good results because there is a diversity between different classifiers. Moreover, the proposed segmentation model achieved good results when some parts of the ear images are occluded.

New data-hiding algorithm based on adaptive neural networks with modified particle swarm optimization

A new steganography algorithm based on five protection layers has been suggested in this paper for embedding a large amount of secret messaging in a color image, as represented in the spatial domain. The suggested hiding algorithm employs an impressive image segmentation algorithm that uses two levels of adaptive non-uniform segmentation (TLANUS) to embed data randomly instead of sequentially. For each byte and for each color in a cover image, a non-uniform number of bits to be replaced by secret bits is imposed, depending on the byte characteristic's assessment using a weighting factor (ω) that is created from a cipher key (ck) to damp the difference in the surrounding twelve high bytes for the current byte (b). A learning machine has been proposed to adjust a pixel's value; this machine is based on an adaptive neural network (ANN) with modified particle swarm optimization (MPSO). However, the PSO modification has been introduced using a second-order differential equation (SODE); this approach is applied to optimize the positions of the particles, where the adaptive finite-element method (AFEM) is used to find the approximate solution of the SODE. The experimental results have been discussed regarding different performance measures; these measures have demonstrated the effectiveness of the proposed steganography algorithm with the machine learning ANN_MPSO in terms of its embedding capacity and imperceptible level. Comparisons between the proposed approach and the wide spectrum of steganographic schemes have been implemented. The results confirm that the stego-image with high imperceptibility has been reached even if the stego-image holds a large amount of data that reaches twelve bits per pixels (12-bpp) at certain bytes. In addition, it is confirmed that the proposed algorithm can embed secret data efficiently with better visual quality and working under rich image models to confirm that the present approach is resistant to attacks.

A Novel Approach for Color Image, Steganography Using Nubasi and Randomized, Secret Sharing Algorithm

In this paper, we present a new approach for concealing secret message in a digital image by adding three layer securities. In the first layer, the secret image is encrypted by using the famed AES algorithm. In the second layer, we establish a Non-Uniform Block Adaptive Segmentation on Image (NUBASI) algorithm for generating segments of the cover image. This algorithm accept a Key from sender and by using this key, split the cover image into small pieces each with different dimension. In the third layer, a new randomized secret sharing algorithm hide the secret data into the different segments. This algorithm defines 32 different patterns for selecting the segments order to implant the cipher data. This proposed method was implemented and tested for various size cover images and secret messages. The quality of the final stego-image and original image are compared and analyzed through their PSNR values.

Accurate Fixed-Point Logarithmic Converter

The hardware computation of the logarithm function is required in several applications, ranging from signal and image processing to telecommunication systems. This brief shows that most of previous proposed logarithmic converters, based on piecewise linear approximations, suffer from large errors when dealing with fixed-point input values with many fractional bits, a situation often encountered in practical applications. Thus, this brief proposes a novel logarithmic converter, using nonuniform segmentation and piecewise linear approximation. A rigorous technique that allows computing the optimal segmentation and the coefficients values for a prescribed precision is described in this brief. For fixed-point input values, the proposed approach allows obtaining a sensibly lower error, for the same number of nonuniform segments, compared with previously published results. Implementation details and synthesis results in a 65-nm CMOS technology are also presented.

Design of Hardware Function Evaluators Using Low-Overhead Nonuniform Segmentation With Address Remapping

In the piecewise function evaluation with polynomial approximation, nonuniform segmentation can effectively reduce the size of lookup tables for some arithmetic functions compared to uniform segmentation approaches, at the cost of the extra segment address (index) encoder that results in area and delay overhead. Also, it is observed that the nonuniform segmentation reflects a design tradeoff between the ROM size and the area cost of the subsequent arithmetic computation hardware. In this paper, we propose a new nonuniform segmentation method that searches for the optimal segmentation scheme with the goal of minimized ROM, total area, or delay. For some high-variation arithmetic functions, the proposed segmentation method achieves significant area reduction compared to the uniform segmentation method. We also demonstrate the design tradeoff among uniform and nonuniform segmentation, and degree-one and degree-two polynomial approximations, with respect to precision ranging from 12 to 32 bits for the elementary function of reciprocal.

A dynamic non-uniform segmentation method for first-order polynomial function evaluation

This paper presents a new dynamic non-uniform segmentation method for the first-order polynomial function evaluation. The proposed method can evaluate the elementary functions (e.g. log, exp, sin, cos, tan, etc.) and combinations of these functions by an optimized linear approximation with the fewest non-uniform segments. Compared with the previous evaluation method based on the static bit-width analysis, the proposed method is mainly based on a dynamic bit-width analysis and capable of reducing the number of segments, which in turn can significantly reduce the memory size occupied in hardware. The proposed dynamic method can evaluate the function to satisfy accuracy by the linear approximation in which the input, coefficients, and intermediate values are rounded to fewer bit-width, which cannot be achieved by previous static non-uniform segmentation methods. The hardware performance evaluation results on FPGA show that the proposed method consumes about 66% fewer hardware resources, 56% less actual memory usage, and performs 32% shorter delay on average in comparison with the non-uniform segmentation method based on static bit-width analysis.

A Rapid Separation Method for Nonuniform Image Segmentation

It is difficult to separate objects from an image when its background is nonuniform. Traditional methods tend to get obvious targets by using many different algorithms, such as Ostu, morphology, etc. But it frequently fails in extracting objects with different size and shape in nonunfirom background. A new method is proposed for nonuniform image segmentation in this paper. First, on an initial image, grid sample method is performed to reduce data space and prepare for background estimation and an example image is formed by those grids. Then, Gaussian Low Pass Filter (GLPF) is used to filter the noise point in the small image. Then, the next step is to magnify the area of this example image through an interpolation algorithm. Facet Model is used to estimate the background image. Finally, the object image can be acquired by the initial image substracting this estimated background image. Experiments are performed and according to the results, the validity and adaptability of the method is enhanced obviously, compared with conventional image segmentation algorithms.

Direct Digital Frequency Synthesizer Using Nonuniform Piecewise-Linear Approximation

This paper investigates a novel direct digital frequency synthesizer architecture, based on piecewise linear approximation with segments of nonuniform length. The new approach allows reducing the total number of segments with respect to the well-known uniform segmentation. In this way the size of the coefficient ROM is also reduced with beneficial effects in terms of speed and power. We show that the optimal nonuniform segmentation (that maximizes the spurious-free dynamic range for a given number of nonuniform segments) can be obtained as the solution of a mixed-integer linear programming problem. Three simple, suboptimal, nonuniform segmentation schemes (which lend themselves to efficient hardware implementation) are proposed in this paper. We present also several design examples and VLSI implementation results, which demonstrate the effectiveness of the developed technique.

A Study of Color Space Transformation Method Using Nonuniform Segmentation of Color Space Source

Color space transformation is the process of converting color from one space to the other type of color space, which plays a very important role in image acquisition, display and color information transfer. Though CMYK space is applied to express color when using printer or phototypesetter, editing correction in CMYK color space may cause color loss and lower the speed of computer operation. Thus, it is often converted into CIE L*a*b* uniform color space which is device-independent. In general, there existed a problem of that the dark tone has a big color difference in previous methods when doing color transformation. In order to handle such issue, we put forward a sampling method for color space with the non-uniform segmentation, which utilizes multiple regressions, prism interpolation and pyramid interpolation to model the color space for the same color inkjet. The technical difficulties of the three methods and their implementations are introduced in detail, together with the realizations from CMYK space to CIE L*a*b* space. The comparisons among such three methods are carried out by horizontal-vertical way. By analyzing the modelling results, it clearly shows that the proposed way for color space transformation can get better effect by using polynomial regression method and three-dimensional modelling method. The experiment results also indicate that the introduced approach is a kind of good converting way for color space with less color difference, better effect and much simpler, and it can be easily made widespread into other color space transformation.

FPGA-implementation of atan(Y/X) based on logarithmic transformation and LUT-based techniques

This paper presents an architecture for the computation of the atan ( Y / X ) operation suitable for broadband communications systems where a throughput between 20 and 40 MHz is required. The proposed architecture implements a division operation of two inputs by means of a logarithmic transformation, in which the division can be performed with a subtraction. A combination of non-uniform segmentation and multipartite LUT technique is proposed for the arctangent of the logarithm approximation. The architecture was implemented in a Xilinx FPGA device achieving higher throughput than the approach based on CORDIC algorithm and lower area than previous LUT-based approaches.