nils-herrmann/clean_scirepeval huggingface dataset:
Solving inverse kinematics of delta robot using ANFIS

The paper propose a methodology using the ability of ANFIS (Adaptive Neuro-Fuzzy Inference System) to solve inverse kinematics problem of Delta parallel robot. A five layer neural network of ANFIS is used to adjust input and output parameters of membership function in a fuzzy logic controller. The hybrid algorithm is used for training this network. In this algorithm, the least square estimation method is applied for the tuning of linear output membership function parameters and the error backpropagation method is used to tune the nonlinear input membership function parameters, it is possible to predict position end-effector exactly against inverse kinematics and reduce mathematical representation of the system. Virtual Reality (VR) is used to simulation trajectory of robot. Computer simulations with Matlab ver. 2013b conducted on 3DOF robot Delta shows the effectiveness of the approach.
Digital Post-Compensation of Nonlinear Distortions in Wavelength Conversion Based on Four-Wave Mixing in a Semiconductor Optical Amplifier

We present a novel low-complexity digital filter-based backpropagation (DFBP) technique to compensate the nonlinear distortions induced by a wavelength converter based on four-wave mixing in a semiconductor optical amplifier. We first develop the DFBP based on a small-signal approximation and probe performance via numerical simulations. We also experimentally demonstrate that application of DFBP to wavelength conversion of 10 Gbaud 16-QAM and 5 Gbaud 64-QAM signals significantly improves the error-vector magnitude and the bit error rate when the wavelength converted signal is limited by the nonlinear distortions.
Interactions of Prostate Cancer Cells to Human Microvascular Endothelial Cells Under Shear Stress Conditions

The interactions between prostate cancer cells with endothelial cells in the microvessels play an important role in prostate tumor metastasis. However, not many studies have been conducted to investigate the adhesion of prostate cancer cells on vascular endothelium and related mechanisms and modulators. In this study, human microvascular endothelial cells (HMEC) grown on glass slides were exposed to two different circulating prostate cancer cells, PC-3 and PC-3ML (highly metastasis prostate cancer cells) using an in vitro parallel flow system. The gene profiles of HMEC, after exposure to prostate cancer cells, were also investigated using cDNA microarrays. It was found that PC-3ML cells had significantly higher adhesion than PC-3 cells on HMEC monolayer. In addition, PC-3ML cells triggered more gene changes of HMEC than PC-3 cells did. These results suggested that prostate cancer cells might increase their adhesion to vascular endothelial cells by affecting the endothelial cell on the gene expression level.
Performance comparison of a planar substrate-integrated Fabry-Perot cavity antenna with different unit cells at terahertz frequency

In this paper, we investigate and compare the radiation characteristics of a planar substrate-integrated Fabry-Perot cavity antenna designed with different unit-cell geometries. The antenna consists of a frequency selective surface (FSS) and a planar feeding structure, which are both lithographically patterned on a high-permittivity substrate. The planar feeding structure is an open-ended leaky-wave slit with a large ground plane, while the FSS is a 7 × 7 array of unit cells. Antennas with FSSs using different unit-cell geometries were studied at a frequency of around 0.315 THz. The numerical results showed that the FSS with square-patch unit cells achieved the best maximum gain of 14.3 dBi, while the FSS with circular-patch unit cells produced the best 3-dB gain bandwidth of 3%.
Hidden Markov tree image denoising with redundant lapped transforms

Hidden Markov tree (HMT) wavelet models have demonstrated superior performance in image filtering, by their ability to capture features across scales. Recently, we proposed to extend the HMT framework to the lapped transform domain, where lapped transforms (LT) are M-channel linear phase filterbanks. When the number of channels is a power of 2, the block partition provided by LT is remapped to an octave-like representation, where an HMT is able to model the statistical dependencies between intra- and interband coefficients. Due to better energy compaction and reduced aliasing properties, LT outperforms discrete wavelet transforms at moderate noise levels, both subjectively and objectively. However, critically-decimated LT suffers from a lack of shift-invariance, resulting in a degraded performance. We study the improvement of HMT modeling in the LT domain (HMT-LT), combined with a redundant decomposition, in order to increase its performance for image denoising.
LCD Motion Blur: Modeling, Analysis, and Algorithm

Liquid crystal display (LCD) devices are well known for their slow responses due to the physical limitations of liquid crystals. Therefore, fast moving objects in a scene are often perceived as blurred. This effect is known as the LCD motion blur. In order to reduce LCD motion blur, an accurate LCD model and an efficient deblurring algorithm are needed. However, existing LCD motion blur models are insufficient to reflect the limitation of human-eye-tracking system. Also, the spatiotemporal equivalence in LCD motion blur models has not been proven directly in the discrete 2-D spatial domain, although it is widely used. There are three main contributions of this paper: modeling, analysis, and algorithm. First, a comprehensive LCD motion blur model is presented, in which human-eye-tracking limits are taken into consideration. Second, a complete analysis of spatiotemporal equivalence is provided and verified using real video sequences. Third, an LCD motion blur reduction algorithm is proposed. The proposed algorithm solves an l1-norm regularized least-squares minimization problem using a subgradient projection method. Numerical results show that the proposed algorithm gives higher peak SNR, lower temporal error, and lower spatial error than motion-compensated inverse filtering and Lucy-Richardson deconvolution algorithm, which are two state-of-the-art LCD deblurring algorithms.
Optimal spatial-temporal weight prediction for inter-frame coding of H.264/AVC video sequences

The paper proposes a novel method for optimal weight prediction in coding inter-frame of H.264/AVC compressed video sequences. The weights are optimized so that the motion compensated frames is closest to the current frame. This minimizes the residual frame and thus helps reducing the number of bits required to code the residual frame. We first consider the general optimal spatial-temporal weight prediction and then specify the special solution in case of temporal weight prediction using two reference frames (bi-predictive coding). Simulations on a wide range of video sequences are performed to verify the effectiveness of the algorithm on both visual quality and rate-distortion relation.
Linear-phase M-band wavelets with application to image coding

This paper investigates the design of M-band linear phase wavelet filter banks (M>2), and explores their application to image coding. The generalized LOT description of M-band linear-phase paraunitary filter banks is used to parametrize the M-band linear-phase orthogonal wavelets. It is proven that an M-band linear-phase orthogonal wavelet of even length cannot have more than one vanishing moment. Since this limits the effectiveness of the resulting wavelet filters, we next suggest methods for the construction of linear-phase biorthogonal M-band wavelet lowpass filters, generalizing prior 2-band constructions. However, one cannot guarantee that an arbitrary lowpass filter pair can be completed to a full perfect-reconstruction filter bank. Finally, the new linear-phase orthogonal wavelet filter banks are compared with known wavelet filters with regard to their performance in a transform-based image coder.
IMC-PID approach: An effective way to get an analytical design of robust PID controller

In this paper, the analytical PID controller design methodology of the SISO and MIMO processes has been discussed on the basis of the IMC principle. The paper deals with two issues: firstly emphasizes on a tuning method of a SISO stable and unstable process and secondly a MIMO tuning rule. To verify the superiority of the proposed tuning method, simulation studies have been conducted on several process models. The results show that the proposed tuning method has a convincing result for wide classes of process models. The robustness analysis is also carried out by inserting a perturbation in each of the process parameters simultaneously, with the results demonstrating the better robustness of the proposed controller design with parameter uncertainty.
Compression artifact reduction based on total variation regularization method for MPEG-2

In accordance with the recent improvement in the quality of image displays for consumer electronics, digital image compression artifacts are visible than ever. Noise removable methods are key issues not only in research areas such as image sensing and satellite imaging but also in consumer electronics such as television signal conversion, DVD and Blu-ray encodings. Among various compression artifact removable methods, the Total Variation (TV) regularization method is one of the most promising solutions. However, there are two problems: one is loss of image minuteness in the texture components, and the other is insufficient compression artifact removal in low bit rates. In this paper, we propose a new noise removable method utilizing the TV regularization method for moving pictures compressed by MPEG-2. We obtain fine experimental results in image quality improvement, and our method is possible to be applied to the other DCT based-image compression standards such as MPEG-4 and H.264.
Single image spatially variant out-of-focus blur removal

This paper addresses an out-of-focus blur problem in which the foreground object is in focus whereas the background scene is out of focus. To recover the details of the background scene, a spatially variant blind deconvolution problem must be solved. However, spatially variant deconvolution is computationally intensive because Fourier-based methods cannot be used to handle spatially variant convolution operators. The proposed method exploits the invariant structure of the problem by first predicting the background. Then a blind decon-volution algorithm is applied to estimate the blur kernel and a coarse estimate of the image is found as a side product. Finally, the background is recovered using total variation minimization, and fused with the foreground to produce the final deblurred image.
Non-negative scaling functions and applications in image coding

At medium and low bit rates, traditional subband image coding algorithms suffer from basis related visual artifacts. Short basis causes blocking and long basis causes ringing. In particular, ringing is caused by the undershoot and overshoot of time domain response of the basis functions. They can be effectively suppressed by using a scaling function that monotonically decreases to zero at block edges. In this paper, the theory, structure and design of filter banks with nonnegative scaling function are analyzed and their application in image coding is investigated. Experiments show that both blocking and ringing noises can be effectively suppressed by using the proposed filter banks. Moreover, they can be parametrized by lattice structures.
Multicriterion optimized QMF Bank design

The quadrature mirror filter (QMF) bank with multicriterion constraints such as minimal aliasing and/or minimal error coding is among the most important problems in filterbank design, for solving which linear algebra-based methods are still heuristic and do not always work, especially for large filter length. It is shown in this paper that this problem can be reduced either to convex linear matrix inequality (LMI) optimization (when filters are of nonlinear phase) or to semi-infinite linear (SIP) programming (when filters are of linear phase), which can be very efficiently solved either by the standard LMI solvers or our previously developed SIP solver. The proposed computationally tractable optimization formulations are confirmed by several simulations.
Discrete coefficients filter banks and applications in image coding

Discrete coefficient filter banks reduce the computational complexity for subband image coding systems. A new time domain formulation is proposed for the design of near-perfect reconstruction filter banks with discrete coefficients. It can be shown that the proposed design method yields an l/sup 2/ optimal solution which is considered to be practical in image coding applications. In addition, special emphasis is placed in designing filter banks with nonnegative scaling functions that smoothly decay to zero at block edges in order to reduce basis related artifacts. As a result, the reconstructed image does not suffer from blocking and ringing artifacts at medium bit rate (25:1 compression at 0.32 bit per pixel.).
Development of the simultaneous mapping system for the robotic fish

This paper describes a mapping system for robotic fish in partially structured environments. Several infrared sensors are used to obtain information about the structures in such environments. An algorithm has been developed to extract object features, together with their uncertainty, from infrared sensors. The obtained information is incorporated into a feature based mapping algorithm running an extended Kalman filter. Simultaneously, robotic fish's position is provided to the feature extraction algorithm to correct the disturbance that the robotic fish motion produces in the mapping process. Moreover, the following procedures which are the build and maintaining the sequence of local maps as well as posteriorly recovering the full global map has been adapted for the presented application. Traditional robots have rigid underlying structures that limit their ability to interact with their environment. However, the robotic fish has the flexible body and the efficient in using power thus the robotic fish is a good selection for simultaneous mapping.
LCD motion blur modeling and simulation

Liquid crystal display (LCD) devices are well known to have slow response due to the physical limitations of the liquid crystals. Therefore, fast moving objects in a scene are often seen blurred on an LCD. In order to reduce motion blur, an accurate LCD model and an efficient deblurring algorithm are needed. However, existing LCD models are inadequate to reflect the human eye tracking limitation. Also, the spatial-temporal equivalence in LCD models is widely used but not proven directly in the 2D discrete spatial domain. In this paper, we study the human eye tracking limit by reviewing a number of papers in the cognitive science literature. We provide both theoretical and experimental arguments to support our findings. Also, we prove the spatial-temporal equivalence rigourously and verify the results using real video sequences.
A simple design method for nonuniform multirate filter banks

The theory and design of perfect-reconstruction uniform-band QMF banks have been extensively studied. For nonuniform-band filter banks, there is a lack of efficient design methods. We propose a simple design method for nonuniform integer-decimated filter banks (near perfect reconstruction). The approach is to transform the nonuniform filter bank design problem to a corresponding uniform filter bank design problem. Therefore, only one prototype filter needs to be designed. The analysis and synthesis filters are simple combinations of the cosine modulated versions of the prototype filter. An example is given to demonstrate the proposed method.<<ETX>>
Improved technique for design of perfect reconstruction FIR QMF banks with lossless polyphase matrices

A technique is developed for the design of analysis filters in an M-channel maximally decimated, perfect reconstruction, finite-impulse-response quadrature mirror filter (FIR QMF) bank that has a lossless polyphase-component matrix E(z). The aim is to optimize the parameters characterizing E(z) until the sum of the stopband energies of the analysis filters is minimized. There are four novel elements in the procedure reported here. The first is a technique for efficient initialization of one of the M analysis filters, as a spectral factor of an Mth band filter. The factorization itself is done in an efficient manner using the eigenfilters approach, without the need for root-finding techniques. The second element is the initialization of the internal parameters which characterize E(z), based on the above spectral factor. The third element is a modified characterization, mostly free from rotation angles, of the FIR E(z). The fourth is the incorporation of symmetry among the analysis filters, so as to minimize the number of unknown parameters being optimized. The resulting design procedure always gives better filter responses than earlier ones (for a given filter length) and converges much faster.
On multi-scale feature detection using filter banks

A discrete filtering framework is proposed for multiscale feature detection. The approach starts by choosing the sampling rate for the highest resolution filter of a given signal. Subsequently, a bank of filters for a multiscale representation of that signal is designed by least-square approximation. With this signal representation, multiscale maximum a-posteriori (MAP) detectors are designed for the detection of specific features, such as impulses and edges, without specific knowledge of the signal, the appropriate scale of detection and noise. The approach enables the detection of features with very little or no prior information.
A parallel SIMD implementation of single image defogging

The increase of video cameras on phones, privately owned unmanned aerial vehicles, and now automobiles increase the demand for improved subjective quality in hazy and foggy weather. The presence of fog and haze reduce the local contrast of scenes which reduces the ability of humans to detect important objects. The most common method now to improve the contrast is to employ a single image defogging algorithm. Single image defogging has been a research topic of interest for the past seven years with newer advancements in complexity and color enhancements. There exists many methods with variations on the same theme of estimating a transmission map (thickness of fog) first and then removing the fog while respecting the true color of the scene. The methods that do exist, however, do not consider the algorithmic complexity constraints when implementing on a System on Chips (SoC). In our work, we demonstrate a single image defogging capability for live video on a SoC with parallel SIMD engine. We present the trade-offs and complications that need to be addressed for single image defogging methods for SIMD architecture hardware.
Adaptive Directional Wavelet Transform Based on Directional Prefiltering

This paper proposes an efficient approach for adaptive directional wavelet transform (WT) based on directional prefiltering. Although the adaptive directional WT is able to transform an image along diagonal orientations as well as traditional horizontal and vertical directions, it sacrifices computation speed for good image coding performance. We present two efficient methods to find the best transform directions by prefiltering using 2-D filter bank or 1-D directional WT along two fixed directions. The proposed direction calculation methods achieve comparable image coding performance comparing to the conventional one with less complexity. Furthermore, transform direction data of the proposed method can be used for content-based image retrieval to increase retrieval ratio.
Camera-Aware Multi-Resolution Analysis for Raw Image Sensor Data Compression

We propose novel lossless and lossy compression schemes for color filter array (CFA) sampled images based on the <underline>C</underline>amera-<underline>A</underline>ware <underline>M</underline>ulti-<underline>R</underline>esolution <underline>A</underline>nalysis, or CAMRA. Specifically, by CAMRA we refer to modifications that we make to wavelet transform of CFA sampled images in order to achieve a very high degree of decorrelation at the finest scale wavelet coefficients; and a series of color processing steps applied to the coarse scale wavelet coefficients, aimed at limiting the propagation of lossy compression errors through the subsequent camera processing pipeline. We validated our theoretical analysis and the performance of the proposed compression schemes using the images of natural scenes captured in a raw format. The experimental results verify that our proposed methods improve coding efficiency relative to the standard and the state-of-the-art compression schemes for CFA sampled images.
The new combined Maximum Power Point Tracking algorithm using fractional estimation in photovoltaic systems

This paper presents an improved algorithm of quick and accurate Maximum Power Point Tracking (MPPT) algorithm which is based on Incremental conductance algorithm, Fractional Open Circuit Voltage and Short Circuit Current. The proposed algorithm estimates the short circuit current or open circuit voltage, following by using Fractional Short Circuit Current or Fractional Open Circuit Voltage algorithm to quickly determine point close to MPP. MPP will be accurately determined due to Incremental conductance algorithm. The proposed algorithm can identify quickly and correctly MPP when the environmental temperature and solar radiation change. The results of proposed algorithm are made by simulating with MATLAB/Simulink program and experimenting with kit DSpace DS 1104.
Denoising in the lapped transform domain

In this paper, we extend the results of the denoising in the wavelet domain to the lapped transform (LT) domain. The LT coefficients are rearranged into the octave based representation, and their statistics are modeled by the same methods as the wavelet coefficients. Since the part of the LT that follows the discrete cosine transform (DCT) is orthogonal, the problem of removing the quantization noise introduced in the DCT based codecs can be solved in the LT domain. The maximum a posteriori (MAP) estimator is obtained, which provides a novel approach to the image compression artifact reduction problem. The proposed method removes the additive noise and the quantization noise effectively with the significant improvement of image quality.
Leverage a Trust Service Platform for Data Usage Control in Smart City

In the Internet of Thing, data is almost collected, aggregated and analyzed without human intervention by machine-to-machine communications resulting in raising serious challenges on access control. Particularly in Smart City ecosystems in which multi-modal data comes from heterogeneous sources, data owners cannot imagine how their data is used to extract sensitive information. Thus, there is a critical need for novel access control methods that minimize privacy risks while increase ability of personalized access control. Our solution is to build a trust-based usage control mechanism called TUCON that enables stakeholders to set access control policies based on their trust relationships with data consumers. In this study, we introduce two novel paradigms integrated in the Smart City shared platform: a Trust Service Platform and a Data Usage Control, then bring them together to establish the new mechanism. The conceptual model, the architecture, the formalization, and the practical development of TUCON is described in detail. We also show the roles and the interactions of TUCON components in the Smart City platform. Our contributions lie in a new trust model with a trust computation procedure based on semantic web technologies, a novel trust-based usage control conceptual model including a formalization, a practical expression and an architecture for Smart City systems. We believe this study provides better understanding on both trust and usage control in the Internet of Things and opens several important research directions in the future.