nils-herrmann/clean_scirepeval huggingface dataset:
Non-surjective finite alphabet iterative decoders

This paper introduces a new theoretical framework, akin to the use of imprecise message storage in Low Density Parity Check (LDPC) decoders, which is seen as an enabler for cost-effective hardware designs. The proposed framework is the one of Non-Surjective Finite Alphabet Iterative Decoders (NS-FAIDs), and it is shown to provide a unified approach for several designs previously proposed in the literature. NS-FAIDs are optimized by density evolution for WiMAX irregular LDPC codes and we show they provide different trade-offs between hardware complexity and decoding performance. In particular, we derive a set of 27 NS-FAIDs that provide decoding gains up to 0.36 dB, while yielding a memory/interconnect reduction up to 25%/30% compared to the Min-Sum decoder.
Hand Gesture Controlled Drones: An Open Source Library

Drones are conventionally controlled using joysticks, remote controllers, mobile applications, and embedded computers. A few significant issues with these approaches are that drone control is limited by the range of electromagnetic radiation and susceptible to interference noise. In this study we propose the use of hand gestures as a method to control drones. We investigate the use of computer vision methods to develop an intuitive way of agent-less communication between a drone and its operator. Computer vision-based methods rely on the ability of a drone's camera to capture surrounding images and use pattern recognition to translate images to meaningful and/or actionable information. The proposed framework involves a few key parts toward an ultimate action to be taken. They are: image segregation from the video streams of front camera, creating a robust and reliable image recognition based on segregated images, and finally conversion of classified gestures into actionable drone movement, such as takeoff, landing, hovering and so forth. A set of five gestures are studied in this work. Haar feature-based AdaBoost classifier[1] is employed for gesture recognition. We also envisage safety of the operator and drone's action calculating the distance based on computer vision for this task. A series of experiments are conducted to measure gesture recognition accuracies considering the major scene variabilities, illumination, background, and distance. Classification accuracies show that well-lit, clear background, and within 3 ft gestures are recognized correctly over 90%. Limitations of current framework and feasible solutions for better gesture recognition are discussed, too. The software library we developed, and hand gesture datasets are open-sourced at project website.
The space vector PWM for voltage source inverters using Artificial Neural Networks Based on FPGA

This paper presents space vector PWM algorithm for six- switch three-phase inverters (SSTPI) using Artificial Neural Networks (ANN) based on the ready-to-use field-programmable gate array (FPGA) technology. ANN is used to calculate ON-OFF time for six switches when given the angle and modulation index of required voltage space vector. This approach has the advantage of very fast implementation of an Space Vector PWM (SVPWM) algorithm that can increase the converter switching frequency, particularly when the field-programmable-gate-array (FPGA) technology is used in the modulator. This SVPWM is also validated experimentally using FPGA-XUP VIRTEX II PRO from Xilinx in SSTPI-IM system for under modulation mode and over modulation mode 1 and 2, extended to six-step mode, especially for 40khz-switching-frequency case.
Multidimensional 2-channel PR filter banks

General concept of multidimensional (M-D) two-channel perfect reconstruction (PR) filter banks is presented. The framework starts by constructing a factorizable halfband function in M-D and, subsequently, uses spectral factorization to obtain the subband filters. The number of zeros at the aliasing frequency can be obtained easily by using variable transformation from 1-D to M-D. The possible passband shape is discussed. A novel condition for 2-channel PR filter banks in M-D is presented by which the choices of possible passband shapes are significantly reduced. Finally, a frequency mapping between two possible passbands is proposed. A necessary and sufficient condition for the mapping to preserve the PR condition is presented.
Guest Editorial Special Issue On Multirate Systems, Filter Banks, Wavelets, And Applications

The last decade has seen a tremendous amount of 
activity and emergence of applications in the areas of 
filter banks and wavelets. These topics are of such wide 
interest that there have been papers in many different journals, conferences, and workshops in diverse disciplines. However, many aspects of the theory, design, and application of filter banks and wavelets are of great interest to the circuits and systems community as well. Our editorial team felt that this was a perfect time to put together a special issue with state-of-the-art papers on these popular topics. All of the papers have been peer-reviewed according to the usual practice of 
this TRANSACTIONS. Almost in parallel, there is also a similar 
special issue (April 1998) by the IEEE TRANSACTIONS ON SIGNAL 
PROCESSING, with a slightly greater emphasis on applications. 
Many well-known authors have contributed articles to these 
special issues and we expect these to serve as valuable 
references for a long time to come.
On the Fixed-Point Accuracy Analysis of FFT Algorithms

In this paper, we investigate the effect of fixed-point arithmetics with limited precision for different fast Fourier transform (FFT) algorithms. A matrix representation of error propagation model is proposed to analyze the rounding effect. An analytic expression of overall quantization loss due to the arithmetic quantization errors is derived to compare the performance with decimation-in-time (DIT) and decimation-in-frequency (DIF) configurations. From the simulation results, the radix-2 DIT FFT algorithm has better accuracy in term of signal-to-quantization-noise ratio (SQNR). Based on the results, a simple criterion of wordlength optimization is proposed to yield comparable accuracy with fewer bit budget.
Image denoising by targeted external databases

Classical image denoising algorithms based on single noisy images and generic image databases will soon reach their performance limits. In this paper, we propose to denoise images using targeted external image databases. Formulating denoising as an optimal filter design problem, we utilize the targeted databases to (1) determine the basis functions of the optimal filter by means of group sparsity; (2) determine the spectral coefficients of the optimal filter by means of localized priors. For a variety of scenarios such as text images, multiview images, and face images, we demonstrate superior denoising results over existing algorithms.
Analysis of color rendering transformation effects on dehazing performance

Dehazing is an image restoration technique generally applied as a post-processing step in a camera processing pipeline. The performance of dehazing algorithms is strongly dependent on the electro-optics transfer curve of image acquisition. In order to reduce color distortion on restored images, we investigate how color rendering transformations affect the dehazing algorithms in a camera processing pipeline. First, we develop numerical analysis on how a hazy image model is modified by color rendering transformations in a pipeline. Based on the analysis and observations, we conclude that better dehazing performance can be achieved when a dehazing algorithm is applied before color rendering transformations. In addition to the theoretical analysis, experimental results validate our conclusion.
Multi-band color image deblurring using contourlets for fluid lens camera systems

Fluidic lens camera systems present a new field of exploration for both the optics and image processing communities. Developed for surgical applications, these cameras do not have moving parts while zooming and they have better miniaturization possibilities. However, the lens causes non-uniform color blur between color planes which creates an image processing problem. We propose the use of a contourlet filter bank system to deblur color images without estimating a point spread function. This multi-band deblurring method uses sharper color planes to improve blurred ones. Compared to the conventional Lucy-Richardson andWiener filtering, our previous wavelet-based method significantly improves sharpness and ghosting artifacts. The proposed contourlet-based system better adjusts to the natural image contours. This effect produces an image with a similar level of sharpness, but fewer ghosting artifacts. Furthermore, we analyze conditions for when this algorithm will reduce the mean squared error. We also use an Ant Colony Optimization algorithm to detect sharp edges. This algorithm naturally extends to many systems for multi-band deblurring that have high edge correlation.
A correlation based detection method for internal short circuit in battery packs

This manuscript proposes a fault detection method for internal short circuits in battery packs based on the correlation coefficient. Compared to the threshold and model based detection algorithms, the proposed method is more robust to inconsistent battery cells, and does not require any preliminary effort in modeling and online identification. In order to implement this method online, a moving window is selected to maintain its high sensitivity to fault occurrence. A square wave is designed to avoid false detections when the battery is at rest. Experiment results validate the feasibility and demonstrate the advantages of the proposed method.
Orthonormal filter banks with linear phase filters

Maximally decimated perfect-reconstruction filter banks which are used to decompose signals into subbands are discussed. Paraunitary systems in which each individual filter in the analysis and synthesis banks has linear phase are described. Several theoretical questions pertaining to linear phase paraunitary systems are answered. A factorization for such systems that is minimal as well as complete is presented. The number of parameters in the optimization process is reduced by structurally imposing the additional condition that the filters satisfy pairwise mirror-image symmetry in the frequency domain. Examples of M-band linear phase orthonormal wavelets are also presented.<<ETX>>
Performance analysis of motion-compensated de-interlacing systems

A lot of research has been conducted on motion-compensated (MC) de-interlacing, but there are very few publications that discuss the performances of de-interlacing quantatively. The various methods are compared through their performance on known video sequences. Linear system analysis of interlaced video and de-interlacer are proposed in . It is well established that the performance of the MC methods outperform the fixed or motion-adaptive methods when the motion vectors used are reliable and true to the scene content. Being an open-loop process the performance of the MC de-interlacers degrade drastically when there are motion vector errors. In this paper, a linear system analysis of MC video upconversion systems is presented and the effects of motion vector accuracy on system performance are analyzed. We investigate the various factors that contribute to the motion vector inaccuracy, such as incorrect motion modelling, acceleration between the frames, and insufficient interpolation kernel
Method and architecture design for motion compensated frame interpolation in high-definition video processing

In this paper, a novel, fast, and low complexity method is proposed for motion compensated frame interpolation (MCFI). Unlike previous works involving high complexity and complicated time-consuming iterations, the proposed method adopts one-pass and low-complexity approach without any repeated iteration and is capable of dealing with high definition (HD) video processing. The proposed method employs a unique true motion engine that explores at most nine motion candidates with different motion directions and then determines one true motion by referring to neighboring spatial information as well as temporal information. Besides, the proposed method introduces an adaptive overlapped block matching algorithm to enhance the searching accuracy and visual quality. An architecture design is also proposed, which employs a modified multi-level successive eliminate algorithm (MSEA) and has capability to reduce the heavy computation of full search. Experimental results show that the proposed algorithm provides better video quality than conventional methods and shows satisfying performance for HD1080p 30fps at 180 MHz or HD720p 30fps at 83 MHz.
ECG beat detection using filter banks

The authors have designed a multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG). The algorithm incorporates a filter bank (FB) which decomposes the ECG into subbands with uniform frequency bandwidths. The FB-based algorithm enables independent time and frequency analysis to be performed on a signal. Features computed from a set of the subbands and a heuristic detection strategy are used to fuse decisions from multiple one-channel beat detection algorithms. The overall beat detection algorithm has a sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database. Furthermore this is a real-time algorithm since its beat detection latency is minimal. The FB-based beat detection algorithm also inherently lends itself to a computationally efficient structure since the detection logic operates at the subband rate. The FB-based structure is potentially useful for performing multiple ECG processing tasks using one set of preprocessing filters.
On two-channel orthogonal and symmetric complex-valued FIR filter banks and their corresponding wavelets

Two-channel orthogonal and symmetric complex-valued FIR filter banks and their corresponding wavelets are investigated. First, the conditions for the filter bank to be orthogonal, symmetric and regular are presented. Then, a complete and minimal lattice structure is developed, which enables a general design approach for filter banks and wavelets with arbitrary length and arbitrary order of regularity. Finally, two integer implementation methods that preserve the perfect reconstruction property are proposed. Their performances are evaluated via experimental results.
Low Complexity Scalable Video Coding

In this paper, we consider scalable video coding (SVC) which has higher complexity than H.264/AVC since it has spatial, temporal and quality scalability in addition to H.264/AVC functionality. Furthermore, inter-layer prediction and layered coding for spatial scalability make motion estimation and mode decision more complex. Therefore, we propose low complexity SVC schemes by using current developing SVC standard. It is archived by prediction method such as skip, direct, inter-layer MV prediction with fast mode and motion vector (MV) estimation at enhancement layer. In order to increase the performance of inter-layer MV prediction, combined MV interpolation is applied with adjustment of prediction direction. Additionally, fast mode and MV estimation are proposed from structural properties of motion-compensated temporal filtering (MCTF) to elaborate predicted macro block (MB) mode and MV. From the experimental results, proposed method has comparable performance to reference software model with significant lower complexity.
The design of digital quadrature demodulators using the Parks-McClellan algorithm

New results are presented concerning the design of digital quadrature demodulators based on quadrature mixing and lowpass filtering concepts. The use of a 4:1 ratio between the sampling rate and intermediate frequency reduces the computational cost, but also causes the inphase and quadrature filters to become odd and even length filters, respectively. Consequently, the mismatch between the passband gains of the filters becomes an important source of error. The performance data presented in this paper shows that the Parks-McClellan (1973) algorithm can be used to design pairs of I and Q filters whose passband frequency responses are closely matched relative to the size of the passband ripple. However, this favourable result is dependent on the choice of the design specification and the number of filter coefficients.
Iterative R-D optimization of H.264

In this paper, we apply the primal-dual decomposition and subgradient projection methods to solve the rate-distortion optimization problem with the constant bit rate constraint. The primal decomposition method enables spatial or temporal prediction dependency within a group of picture (GOP) to be processed in the master primal problem. As a result, we can apply the dual decomposition to minimize independently the Lagrangian cost of all the MBs using the reference software model of H.264. Furthermore, the optimal Lagrange multiplier lambda* is iteratively derived from the solution of the dual problem. As an example, we derive the optimal bit allocation condition with the consideration of temporal prediction dependency among the pictures. Experimental results show that the proposed method achieves better performance than the reference software model of H.264 with rate control for given bit constraint.
Occlusion boundary detection using an online learning framework

In this work, a novel occlusion detection algorithm using online learning is proposed for video applications. Each frame of a video is considered as a time-step for which pixels are classified as being either occluded or non-occluded. The Hedge algorithm is employed to determine weights for a set of experts, each of which is tuned to detect a specific type of occlusion boundary. In contrast to previous training-based methods, the proposed algorithm does not require any training, and has a runtime linear with respect to the number of experts considered. Detection performance is excellent on novel video sequences for which training data does not exist. In addition, the proposed algorithm is easily extended to provide classification results supplementary to detection. We demonstrate results on a series of challenging video sequences including a dataset of hand-labelled occlusion boundaries.
Localized filtering for artifact removal in compressed images

The paper proposes a novel method for coding artifact reduction in compressed images. For removing blocking artifacts, a localized DCT-based filter with condition on the similarity between surrounding blocks is considered. To reduce ringing, a localized fuzzy filter is utilized to avoid the blurry effect of linear filter and painting-like effect of conventional fuzzy filter. To enhance chroma components and reduce the color bleeding, the localized filter for luma component are implemented for the chroma components. Simulations on a wide range of compressed images are performed to verify the effectiveness of the algorithm.
Crosstalk modeling, analysis, simulation and cancellation in passive-type stereoscopic LCD displays

Passive-type stereoscopic LCD display (or polarized 3D TV) generates 3D effect using quarter-wave patterned retarders and polarized glasses. However, passive-type stereo-scopic displays are susceptible to an undesirable effect called “crosstalk”, which degrades the 3D effect and causes increased eye strain and nausea. In this paper, crosstalk in passive 3D LCD is modeled, analyzed and simulated by implementing extended Jones matrix method. We present how the display intensity changes with viewing angles and simulate stereo images with crosstalk. Furthermore, a method of crosstalk cancellation based on linear programming in YCbCr domain is proposed. Results of our proposed method show that zero crosstalk can be achieved in passive 3D displays with less contrast loss compared with other methods.
Lossless compression of CFA sampled image using decorrelated Mallat wavelet packet decomposition

This paper presents a rigorous analysis of wavelet transform on color filter array (CFA) sampled images. The presented analysis suggests that the wavelet coefficients of HL and LH subbands are highly correlated. Hence, we propose a novel lossless compression scheme for CFA sampled images using the decorrelated Mallat wavelet packet decomposition. We validated our theoretical analysis and the performance of the proposed compression scheme using images of natural scenes captured in a raw format. The experimental results verify that our proposed method improves coding efficiency relative to the standard and the state-of-the-art lossless compression schemes CFA sampled images.
Robust and reduced-order filtering: new characterizations and methods

Several challenging problems of robust filtering are addressed in this paper. First of all, for robust filtering problems, we exploit a new LMI (linear matrix inequality) characterization of minimum variance or H/sub 2/ performance, and demonstrate that it allows the use of parameter-dependent Lyapunov functions while preserving tractability of the problem. The resulting conditions are less conservative than earlier techniques, which are restricted to a fixed, that is parameter-independent, Lyapunov function. The rest of the paper is focusing on the reduced-order filter problems. New LMI-based nonconvex optimization formulations are introduced for the existence of reduced-order filters. Then, several efficient optimization algorithms of local and global optimization are proposed. Nontrivial and less conservative relaxation techniques are presented as well. The viability and efficiency of the proposed tools are confirmed through computational experiments and also comparisons with earlier methods.
Energy tradeoffs for DSP-based implementation of IntDCT

The growing interest in video coding has fostered many research activities towards efficient transform architectures. In the past, several works have been proposed concerning the discrete cosine transform (DCT). However, it has been recently shown how DCT can be computed through successive simple operations, called lifting steps. Besides the reduced complexity, these approaches are very promising since they allow the use of integer arithmetic. In this work we present the design tradeoffs behind the implementation of the IntDCT on two different DSP cores. Particular emphasis has been posed on the energy consumption, being mobile applications our final target.
Using Electrooculogram and Electromyogram for powered wheelchair

This paper is concerned with the development of various ways to control the electrically powered wheelchair. For controlling the wheelchair in the paper, biosignal recognition is employed, and the system of recognition signals as Electromyogram (EMG), Electrooculogram (EOG) has been developed. The main purpose of designed wheelchair allows persons with severe disabilities to reliably navigate a power wheelchair without using the hands. In the biosignal recognition using properties of muscle contractions, and eyes movement are computed and identified control commands by comparing the ranges of the parameters to the statistical range information. The user continuously interacts with the wheelchair and controls the speed and the steering using the EOG and EMG. An obstacle avoidance algorithm has also been developed follow the value of the sensor. This wheelchair based on a standard electrical wheelchair includes an onboard computer, sensors and graphical user interface running on a computer.
A hybrid method to determine the short-circuit location on medium-voltage network: Simulation and measurement

In order to improve stability and power quality of the system, there is an essential role in the rapid detection of grid malfunction, particularly in grids with difficult geographical conditions. This article introduces a method which could quickly delineate short-circuit fault location by establishing the corresponding matrix between nodes on grid and short circuit current value. A 50-nodes local medium-voltage network is calculated. The modeling results will be compared to the value of measurement.