nils-herrmann/clean_scirepeval huggingface dataset:
Forecasting of Solar Photovoltaic System Power Generation Using Wavelet Decomposition and Bias-Compensated Random Forest

The use of solar photovoltaic (PV) power is a promising solution to reduce grid power consumption and carbon dioxide emissions. However, the benefit of utilizing solar PV power is limited by its highly intermittent and unreliable nature. The non-stationary and non-linear characteristic of solar irradiance makes solar PV difficult to predict by traditional time series and artificial intelligence (AI) approaches. To address the above challenges, we propose a novel technique integrating stationary wavelet transform (SWT) and random forest models. Instead of conventional decompose-and-reconstruct process in SWT, we only apply the wavelet decomposition to extract the information from raw data with better time-frequency resolutions. We also propose a bias-compensation technique to minimize the prediction error. Our experimental results using sensors data from the on-campus microgrid demonstrate the proposed approach is robust to different forecast time horizons and has smaller prediction error.
LCD motion blur reduction using fir filter banks

Due to the sample-and-hold nature of Liquid Crystal Display (LCD) image formation, LCDs suffer from motion picture blur. This is especially evident during scenes containing fast motion due to the inherent sample-and-hold nature of LCD image formation. Using models for the Human Visual System (HVS) we take a signal processing approach to solving this problem by pre-processing the data before it is sent to the display. Whereas previous pre-processing approaches either apply a simple high pass filter or an iterative deconvolution algorithm, this work uses a small collection of efficient linear FIR filters to reduce the amount of perceived motion blur. Specifically, we develop a two-channel non-perfect reconstruction filter bank to reduce the motion dependent low pass effects of the HVS. Perceptual tests indicate that our algorithm reduces the amount of perceived motion blur on LCDs at a lower complexity than the existing deconvolution approach.
Video processing with scale-aware saliency: Application to Frame Rate Up-Conversion

A new method for scale-aware saliency detection is introduced in this work. Scale determination is realized through a fast scale-space algorithm using color and texture. Scale information is fed back to a Discriminant Saliency engine by automatically tuning center-surround parameters. Excellent results are demonstrated for predicted fixations using a public database of measured human fixations. Further evidence of the proposed algorithm's performance is exhibited through an application to Frame Rate Up-Conversion (FRUC). The ability of the algorithm to detect salient objects at multiple scales allows for class-leading performance both objectively in terms of PSNR/SSIM as well as subjectively. Finally, the need for operator tuning of saliency parameters is dramatically reduced by the inclusion of scale information. The proposed method is well-suited for any application requiring automatic saliency determination for images or video.
Combining generic and class-specific codebooks for object categorization and detection

Combining advantages of shape and appearance features, we propose a novel model that integrates these two complementary features into a common framework for object categorization and detection. In particular, generic shape features are applied as a pre-filter that produces initial detection hypotheses following a weak spatial model, then the learnt class-specific discriminative appearance-based SVM classifier using local kernels verifies these hypotheses with a stronger spatial model and filter out false positives. We also enhance the discriminability of appearance codebooks for the target object class by selecting several most discriminative part codebooks that are built upon a pool of heterogeneous local descriptors, using a classification likelihood criterion. Experimental results show that both improvements significantly reduce the number of false positives and cross-class confusions and perform better than methods using only one cue.
IIR M-th band filters with allpass components

In this paper, we propose a new allpass-based structure for the IIR M-th and 2M-th band filters. These filters consist of M allpass filters, and an interpolation filter (sum of two allpasses). Consequently, the proposed structure is very efficient in implementation. By choosing the allpass phase appropriately the resulting phase response of the IIR M-th band filter is approximately linear. An example is designed and compared with FIR M-th band filters.
Real time human tracking using improved CAM-shift

In this paper, a novel approach is introduced for tracking human targets in cases of high influence from complexity of environment. In real world applications, the need of interaction in real time between a tracking system and human plays an important role. In the proposed approach, a RGB-D camera is utilized to acquire the depth information which is considered to define the Depth Of Interest (DOI). This DOI is used to combine with the CAM-shift algorithm in human tracking. The Kalman filter is also implemented to help in predicting the direction of target. Comparing with the original CAM-shift algorithm, our approach performs more accurate and more effective results. The experiment results also show that our system can be implemented for real-time applications as well.
Advanced IMC-PID controller design for the disturbance rejection of first order plus time delay processes

A new analytical method for a proportional-integral-derivative (PID) controller cascaded with a lead/lag filter based on the basis of the renowned internal model control (IMC) theory is proposed for the first-order plus dead time (FOPDT), the integrator plus dead time (IPDT), and the unstable FOPDT processes. Analytical tuning rules for the PID filter controller are derived in the transparent way for enhancing the disturbance rejection. A two degree of freedom (2DOF) control scheme is utilized to cope with both the regulatory and servo problems in a simple manner. Several illustrative examples are conducted for a broad class of time-delay processes and the simulation results demonstrate that the proposed method affords better performances for both the disturbance rejection and set-point tracking in compared with those of recently well-known design methods, since the controllers in the simulation study are all tuned to have the same degree of robustne ss in terms of the maximum sensitivity. To demonstrate the robustness of the controllers, the worst-case model mismatch is introduced by inserting perturbation uncertainty in all process parameters simultaneously. The resulting PID characteristics confirm that the proposed controllers hold greater robustness against perturbation uncertainty.
Optimization of Extracellular Matrix Protein Surface Coating to Enhance Endothelialization on Poly(L-Lactic Acid)

Slow endothelialization (endothelial lining on the arterial surfaces) on poly(L-lactic acid) (PLLA) materials is one of many concerns limiting its use in the vascular system. Surface coating with extracellular matrix (ECM) proteins is a well described method used for modifying surface properties of biomaterials in order to encourage cell growth. However, optimization of ECM proteins to promote the adhesion and growth of Human Aortic Endothelial Cells (HAEC) has not been well-investigated. In this study, we compared the effect of common ECM proteins such as fibronectin, collagen, laminin, vitronectin, and matrigel on the HAEC adhesion and growth on PLLA materials. It was found that fibronectin not only had the highest cell adhesion and proliferation, but also maintained normal cell morphology and endothelial cell function. Moreover, endothelial cells remained better on this surface under physiological shear stress levels. These results suggested fibronectin is the best ECM protein to promote endothelialization on PLLA materials.
A Lattice Structure of Biorthogonal Linear-Phase Filter Banks With Higher Order Feasible Building Blocks

This paper proposes a lattice structure of biorthogonal linear-phase filter banks (BOLPFBs) using new building blocks which can obtain long filters with fewer number of building blocks than conventional ones. The structure is derived from a generalization of the building blocks of first-order LPFBs. Furthermore, the proposed building blocks are applicable for both even and odd number of channels. The resulting FBs have good performance in stopband attenuation and low implementation costs.
Predictive Controller for Mobile Robot Based on Fuzzy Logic

In this paper a control design of a non-holonomic mobile robot with a differential drive is presented based on a new behavior-based fuzzy control method. Automatic control of a mobile robot depends on complex signal processing mechanisms. It is based on behavioral architecture which can deal with uncertainties in unknown environments and has the ability to accommodate different behaviors. Basic behaviors are controlled by specific fuzzy logic controllers. The proposed approach qualifies for driving a robot to reach a target while avoiding obstacles in the dynamic environment. Simulation and experiments are performed to verify the correctness and feasibility of the proposed method. The experiment results demonstrated the feasibility and advantages of this predictive fuzzy control on the trajectory tracking of a mobile robot.
Forecast on vietnam electricity consumption to 2030

This article is aimed to forecast the consumption for Vietnam electricity system (GWH) to 2030. An econometric model with the Cobb Douglas production function is used. Five variables proposed in the forecasting function are GDP, income, population, proportion of industry and service in GDP, and number of households. The forecasting equation is tested in terms of stationary and co-integration to choose meaningful variables and to eliminate the minor ones which account for none or small impacts on the forecast. The results show that: (1) the qualified forecasting equation only includes 03 major variables: the per capita income, the population, and the number of households, (2) with medium scenario of the income, the forecasting consumptions in 2020, 2025, 2030 are 230,195 GWH, 349,949 GWH, 511,268 GWH, respectively. (3) The GDP and the proportion of industry and service in GDP do not make major impacts on this forecasting in Vietnam. The method and the result of this article likely are a typical example for developing countries which have a kind of transforming economy as that in Vietnam.
Multidimensional filter banks design by direct optimization

In this paper, we designed multidimensional filter banks by direct optimization in the frequency domain. The desired properties of a filter bank (such as zero aliasing, perfect reconstruction) are formulated as an objective function of the filter coefficients. The design procedures minimize the objective function by iterative quadratic programming with linear constraints. Our design examples show that the method is flexible towards frequency support configurations, smoothness constraints, and can be used to design multidimensional perfect reconstruction filter banks having up to eight channels.
Adaptive non-local means for multiview image denoising: Searching for the right patches via a statistical approach

We present an adaptive non-local means (NLM) denoising method for a sequence of images captured by a multiview imaging system, where direct extensions of existing single image NLM methods are incapable of producing good results. Our proposed method consists of three major components: (1) a robust joint-view distance metric to measure the similarity of patches; (2) an adaptive procedure derived from statistical properties of the estimates to determine the optimal number of patches to be used; (3) a new NLM algorithm to denoise using only a set of similar patches. Experimental results show that the proposed method is robust to disparity estimation error, out-performs existing algorithms in multiview settings, and performs competitively in video settings.
Nonlinear phase Mth band filter and applications in filter bank design

The definition of a nonlinear phase Mth band filter in the time domain is extended from that of the linear phase Mth band filter. The corresponding contraints in the frequency domain are derived based on the proposed time-domain property. They are the general cases of the well-known linear phase conditions. An eigenfilter design method is discussed and an example is shown to demonstrate the theory. Application in a two-channel filter bank and the dyadic wavelet is also discussed.
Symmetric Orthogonal Complex-Valued Filter Bank Design by Semidefinite Programming

A new design method for complex-valued two-channel FIR filter banks with both orthogonality and symmetry properties is developed. Based on a novel linear matrix inequality (LMI) characterization of trigonometric curves, the optimal design of the perfect reconstruction filter bank is reformulated as a semi-definite programme. The dimension of the resulting semi-definite programme is further reduced by exploiting the strong convex duality. Consequently, the globally optimal solution can be effectively found for any practical filter length and desired regularity order
Design and development of the sorting system based on robot

Sorting is one of the important tasks in production line. Using Robot to increase productivity in automatic sorting system interests a large of researchers. In this paper, we recommend a sorting system based on Robot that can identify the position and properties of objects. The sorting system uses the camera placed on the top of the mechanical conveyor belt. With the images captured by the camera, the software will perform the algorithms to identify the location and characteristics of the object (ei. tomatoes), the data of objects is used by pick and place process that synchronize with controller of robot. The sorting process comprising the steps of: detecting the object, determine the object properties (ei. color, size, shape, ...), locate the object, calculate the actual gripping position, the results of the sorting process will be stored and converted into signals to communicate with the control system of robot to perform the sorting tasks. The algorithms will be experimented on Delta robot 3 D.O.F, results will be analyzed and evaluated to calculate the workspace of Robots, so that the productivity and energy efficiency is appropriate.
Frequency selective surface for directivity enhancement of a terahertz photomixer antenna

An antenna design to increase the directivity of a terahertz (THz) photomixer used as a THz source is presented. The antenna is consisted of a full-wavelength dipole on a GaAs membrane substrate and covered by a frequency-selective surface (FSS). The perfect mirror of ground plane and the partially reflective mirror of FSS form a resonator which produces good transmission characteristics and thus significantly improving the antenna directivity. The cavity in the GaAs membrane substrate has been found to be an important parameter to achieve expected high directivity characteristic when having sufficient large size. We show the antenna geometry, optimization process and the final simulated results of the proposed antenna.
Influence of lens size and shape on the performance of a terahertz traveling-wave stripline dipole antenna

In this paper, the influence of a substrate lens made of high-permittivity material on the overall performance of a traveling-wave stripline dipole antenna is presented. Input impedance and radiation characteristics of the antenna are fully investigated over a broad frequency range up to 5.0 THz. The results show that the lens shape represented by the ratio of the extension length to the lens radius primarily determines the best possible antenna gain and radiation spectral bandwidth. The antenna gain response exhibits an increased level of sensitivity to the lens shape as the lens size increases, and this is particularly important when it comes to optimizing large substrate lenses.
Code-aware quantizer design for finite-precision min-sum decoders

Classically, the quantization of the soft information supplied to a finite-precision decoder is chosen to optimize a certain criterion which does not depend on the characteristics of the existing code. This work studies code-aware quantizers, for finite-precision min-sum decoders, which optimize the noise threshold of the existing family of Low-Density Parity-Check (LDPC) codes. We propose a code-aware quantizer with lower complexity than that obtained by optimizing all decision levels and approaching its performance, for few quantization bits. We show that code-aware quantizers outperform code-independent quantizers in terms of noise threshold for both regular and irregular LDPC codes. To overcome the error floor behavior of LDPC codes, we propose the design of the quantizer for a target error probability at the decoder output. The results show that the quantizer optimized to get a zero error probability could lead to a very bad performance for practical range of signal to noise ratios. Finally, we propose to design jointly irregular LDPC codes and code-aware quantizers for finite-precision min-sum decoders. We show that they achieve significant decoding gains with respect to LDPC codes designed for infinite-precision belief propagation decoding, but decoded by finite-precision min-sum.
Visual odometry for RGB-D cameras for dynamic scenes

In this paper, we propose an accurate estimation of the camera motion in a dynamic environment from RGB-D videos. To better exclude the moving object portion of the scene from the stationary background, we use image segmentation. Next, dense pixel matching between the current and reference color images is performed to construct the 3D point cloud for dense motion estimation. At the end, we perform motion optimization, i.e., to find the combination of motion parameters that minimizes the remainder difference between the reference and the current image. We validate our proposed method across two benchmark sequences and show that our approach is more accurate than the existing solutions. We show that our method reduces the RMSE by 6.55% and 7.16% for stationary and dynamic scenes, respectively.
Design of Two-Channel Filter Bank Composed of Linear Phase IIR Filters

This paper proposes a novel method to design two-channel filter banks composed of exactly linear phase IIR filters. Broadly speaking, the design problem is formulated as a semidefinite program (SDP) of minimal order such that the computational complexity is lower than that of existing optimization-based method. Furthermore, it is more flexible than the maximally flat approach as various filter specifications can be easily incorporated. The applicability and efficiency of the proposed method is demonstrated through several numerical examples.
Perfect reconstruction QMF structures which yield linear phase FIR analysis filters

The authors present a perfect reconstruction FIR (finite-impulse response) linear-phase lattice structure for the two-channel quadrature-mirror-filter (QMF) bank. This structure covers most pairs of perfect reconstruction FIR linear-phase analysis filters which have the same (odd) order. For general number of channels M, conditions are derived which any QMF perfect reconstruction linear phase structure must obey. A design example is presented for the M=2 case.<<ETX>>
Vector Sparse Representation of Color Image Using Quaternion Matrix Analysis

Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain.
Visualization of Message Races in MPI Parallel Programs

As parallel programs become larger and more complex, it is harder to understand and debug parallel programs. If those programs have race conditions, intended or otherwise, it is more difficult to understand and debug. Visualization is widely accepted as a powerful technique to understand and manage the complexity of parallel programs as well as to detect race conditions. This paper presents a new visualization tool for debugging race conditions in message-passing programs. Our visualization aims to provide effective and useful visualization for programmers to understand complex phenomena such as the interaction of concurrent processes, to handle enormous visual information, and to debug message races in MPI parallel programs.
Analysis and Efficient Architecture Design for VC-1 Overlap Smoothing and In-Loop Deblocking Filter

In contrast to the macroblock-based in-loop deblocking filters, the filters of VC-1 perform all horizontal edges (for in-loop filtering) or vertical edges (for overlap smoothing) first and then the vertical edges (for in-loop filtering) or horizontal edges (for overlap smoothing) within a frame, field, or slice. These two filters of VC-1 perform filtering operations on many edges among reconstructed blocks in different processing orders. The entire procedure is very time-consuming and involves high memory access loading for the whole system. This paper analyzes the behavior of VC-1 filters and presents several efficient methods and an integrated architecture design, which involves an overlapped 12times12 block that combines overlap smoothing with in-loop filtering for performance and cost by sharing circuits and system resources. In order to go a step further to efficiently utilize system resources, this paper also presents two other efficient methods, multiple processing order and modified chrominance processing order, which greatly reduce external memory cycles and on-chip memory size for filtered and temporal reconstructed pixels. The specification of the proposed architecture implemented with TSMC 90-nm multithreshold voltage technique has capability to process HDTV1080p 30-fps video and HDTV 2048 &times; 1536 24-fps video at 200 MHz. The same concept is applicable to other video processing algorithms, especially in deblocking filter for video post-processing in a frame-based order.
Implementation of the XY2-100 protocol on low-cost microcontroller

To communicate with a controller board, several deflection systems use the digital XY2-100 protocol which is not equipped on most microcontroller units (MCUs). This paper presents a solution to implement the XY2-100 protocol using an 8-bit low-cost AVR RISC Microcontroller. By taking full advantages of the processor and optimizing the parity bit computation code, the MCU sends the data at the speed up to 3.3 Mbits/s. The program is used to control a galvanometric scanner and this can further be used for various systems such as CNC and 3D printer machines.