nils-herrmann/clean_scirepeval huggingface dataset:
Research on using PID algorithm to control simulation model of regenerative braking system based on driving cycles

This study will calculate and simulate the energy recovered during braking or deceleration of the traditional vehicle (KERU: Kinetic Energy Recovery Unit). Using PID controllers to control the simulation model base on driving cycles such as FTP75, NEDC, EUDC and ECE-R15. Simulation results in Matlab Simulink shown that the energy recovered can reach 4.8 kW.h depending on the velocity at the beginning of deceleration, the variation of the acceleration as well as the duration and distance of the driving cycle.
A progressive transmission image coder using linear phase paraunitary filter banks

This paper presents a fully progressive image coder based on well-optimized M-channel linear phase paraunitary filter banks coupled with several levels of wavelet decomposition of the DC band if needed. The uniform-band transform can now be implemented as an overlapped block transform in a parallel fashion with fast, robust, and efficient lattice structures. Keying only in the transform stage, we are able to obtain the highest performance up to date embedded coder. This result shows that lapped transforms, when carefully designed, are capable of providing superior reconstructed image quality compared to wavelets, both objectively and subjectively.
Adaptive wavelet packet basis for entropy-constrained lattice vector quantizer (ECLVQ)

In this paper, the search algorithm for the best wavelet basis/quantization pair is proposed, while specifying the source coding strategy as entropy-constrained lattice vector quantization (ECLVQ). The presented approach differs from previous works since the wavelet basis found is quasi-optimal but the quantizer set is optimally chosen. In addition, the source coding is extended to quantization plus entropy coding, and the computational complexity is reduced by using the estimation formulas instead of measuring the quantization distortion.
Wavelet-based fractal transforms for image coding with no search

The compression performance of fractal image coding is considered using the wavelet-based fractal coder with no search or classification of the domain blocks. A new partitioning scheme is introduced as a variant of earlier schemes which further improves the compression performance. The wavelet-based fractal transform (WBFT) links the theory of multiresolution analysis (MRA) with iterated function systems (IFS). This not only provides a local time-frequency analysis on (the partitions of) the image using multiresolution representation but also an iterative construction of the same (partitions of the) image using IFS and fixed point theory. A set of experiments and simulations show the potentials of using the WBFT for image coding after uniform quantization and entropy coding of the coefficients of the transform. Possibilities for further improvements are discussed.
Global path planning for autonomous robots using modified visibility-graph

This paper attempts to propose an efficient algorithm for global optimized path planning for autonomous robotics system, particularly land-based mobile robot. The presented approach relies on a modified visibility-graph, a classic roadmap of combinatorial planning in computer geometry which is able to retain the complete-feature as well as to reduce computation time, especially in the situation of numerous obstacles existed in practical environment. The developed technique outperforms the original visibility-graph, which is demonstrated through extensive experimental results.
Motion Vector Field Manipulation for Complexity Reduction in Scalable Video Coding

We propose a motion vector field (MVF) scaling algorithm which includes both up-sampling and down-sampling operations. Through this model, it is possible to obtain all the MVF information required for different spatial resolutions in scalable video coding (SVC) efficiently from a pre-specified spatial layer. There's no need to go through all the computationally intensive motion estimation (ME) operations in order to gather those information. This scheme, of course, saves a huge amount of computational power since ME and rate distortion optimization (RDO) typically are the most time consuming tasks in a generic video codec. In video broadcasting application, the proposed algorithm could provide even better coding efficiency than conventional methods in the rate distortion sense.
Image compression using shift-invariant dyadic wavelets

A new class of wavelet filters, shift-invariant wavelet filters, is proposed for the purpose of image coding. The existing approaches obtain shift-invariant wavelet transform by finding the path in the full decomposition tree that minimizes the shift-variance with respect to a given cost function. This procedure is signal dependent and is inefficient for image coding, since the subband decomposition has to be performed for all shifts of input image during processing time. The shift-invariant wavelet transform proposed has a better shift-invariant property compared with the conventional dyadic wavelet transform without changing the dyadic structure. It is also independent of the input images. Experimental results in image coding show that the shift-invariant wavelet transform has a better energy compaction property. Two bit-allocation schemes, which are suitable for the proposed shift-invariant wavelet transform coding, are proposed and evaluated.
Wavelet shrinkage denoising using paraunitary shift-invariant filter banks

A new design method for shift-invariant (SI) wavelet filter bank (FB) is proposed and applied to wavelet shrinkage denoising. The SI wavelet transform obtained using the proposed method has better SI property than the conventional wavelet transform, which is highly desired in wavelet shrinkage denoising. Furthermore, orthogonality and dyadic structure are preserved, and computational complexity is reduced compared with those using the existing approaches. FB design examples, denoising results, and comparisons to the traditional orthogonal wavelet FB are presented.
Optimum subband filterbank design for radar array signal processing with pulse compression

A front-end radar array processing system that operates in the subband domain is considered. It is assumed that pulse compression will be performed on radar return signals in the subband domain. A cohesive algorithm is presented that optimizes the design of all filters used in the subbanding process. These include the analysis filter (used to decompose return signals into narrowband components), the synthesis filter (used when combining subband components after processing), and the pulse compression generating filter (used to generate subband pulse compression filters from a wideband pulse compression filter). The algorithm attempts to minimize system aliasing noise, frequency distortion, and subband processing operations count.
SDP for 2-d Filter Design: General Formulation and Dimension Reduction Techniques

In this paper, a new technique for designing linear phase 2-D filter based on semi-definite programming (SDP) is proposed. This approach allows the design of 2-D filters with accurate cut-off frequency, subject to hard bounds on the frequency response to be achieved on a standard computer. Using the notion of 2-D trigonometric curves, we generalize the 2-D trigonometric Markov-Lukacs theorem to identify the pass-band and the stop-band in the region of support. The 2-D filter specifications are expressed as linear matrix inequalities. We also exploit convex duality to derive SDP formulations of reduced dimensions. Numerical examples illustrating the advantages of our method are also presented
An augmented Lagrangian method for video restoration

This paper presents a fast algorithm for restoring video sequences. The proposed algorithm, as opposed to existing methods, does not consider video restoration as a sequence of image restoration problems. Rather, it treats a video sequence as a space-time volume and poses a space-time total variation regularization to enhance the smoothness of the solution. The optimization problem is solved by transforming the original unconstrained minimization problem to an equivalent constrained minimization problem. An augmented Lagrangian method is used to handle the constraints, and an alternating direction method (ADM) is used to iteratively find solutions of the subproblems. The proposed algorithm has a wide range of applications, including video deblurring and denoising, disparity map refinement, and reducing hot-air turbulence effects.
Symmetric extension methods for M-channel linear-phase perfect-reconstruction filter banks

The symmetric extension method has been shown to he an efficient way for subband processing of finite-length sequences. This paper presents an extension of this method to general linear-phase perfect-reconstruction filter banks. We derive constraints on the length and symmetry polarity of the permissible filter banks and propose a new design algorithm. In the algorithm, different symmetric sequences are formulated in a unified form based on the circular-symmetry framework. The length constraints in symmetrically extending the input sequence and windowing the subband sequences are investigated. The effect of shifting the input sequence is included. When the algorithm is applied to equal-length filter banks, we explicitly show that symmetric extension methods can always be constructed to replace the circular convolution approach.
Depth-based prediction mode for 3D video coding

With emerging multiview displays there is an interest in a new 3D video (3DV) representation and compression standard, which contains depth information, so that virtual views can be synthesized at the decoder from a sparse set of anchor views. With such a representation the depth map can also be utilized to increase video coding efficiency. In this work, we propose a novel depth-based prediction mode that increases inter-view prediction efficiency. The proposed mode is designed such that the syntax is simple and efficient to signal, and it is compatible with existing H.264/MVC modes. This design allows the encoder to choose between the proposed and existing prediction modes by a simple rate-distortion optimization decision. We show that depending on the depth map quality, the proposed mode can provide up to 5.5% additional bitrate savings over coding the texture video and the depth map separately. We also show that when the depth map is accurate, the proposed codec can achieve gains even over coding the texture video only, using H.264/MVC.
A perceptual based contrast enhancement metric using AdaBoost

This paper is an attempt to explore a human element not easily solved in the image processing communities. The problem statement is vague but important to address. What is a good image? More specifically, if a low contrast image is presented, at what level of enhancement is good enough for a human observer? This of course depends on diverse elements, e.g., personal preference, emotional state, physical impairments, purpose (object recognition), etc. In this paper we present a new contrast enhancement metric (CEM) that is trained using several simple contrast measures and mean opinion scores obtained from human observations. Our goal is to train the algorithm to mimic a human when selecting an image with the best contrast between two images. For example, the algorithm will accept two images of the same scene with differing (unknown) contrast and will choose which of the two images is ‘better’ according to what a human believes is ‘better’.
Scale-Aware Saliency for Application to Frame Rate Upconversion

Our understanding of human visual perception has been paramount in the development of tools for digital video processing. For this reason, saliency detection, i.e., the determination of visual importance in a scene, has come to the forefront in recent literature. In the proposed work, a new method for scale-aware saliency detection is introduced. Scale determination is afforded through a scale-space model utilizing color and texture cues. Scale information is fed back to a discriminant saliency engine by automatically tuning center-surround parameters through a soft weighting. Excellent results are demonstrated for the proposed method through its performance against a database of measured human fixations. Further evidence of the proposed algorithm's performance is demonstrated through an application to frame rate upconversion. The ability of the algorithm to detect salient objects at multiple scales allows for class-leading performance both objectively, in terms of peak signal-to-noise ratio/structural similarity index, and 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.
Stochastic Model on the Post-Fabrication Error for a Bragg Reflectors Based Photonic Allpass Filter

This paper presents an analysis of the post fabrication performance for a Bragg-mirrors-based photonic allpass filter. The development subsequently leads to a first definition of the stochastic measures in a realistic system implementation. A Digital Signal Processing (DSP) approach to examining photonic integrated circuitry is explored in depth. The physical origins of error that manifests in an unbalanced phase error is mathematically described in detail. We describe the details of error analysis and demonstrate a stochastic measure for the expected behavior of first-order allpass filter as well as high complexity systems. Our findings show that the frequency performance under error can be characterized by a convolution of the ideal response with a probability function based kernel.
Targeted video denoising for decompressed videos

The paradigm of using clean patches from a targeted external database to design optimal denoising filters, called Targeted Image Denoising (TID), has been shown to outperform state-of-the-art denoising algorithms such as BM3D. In this paper, we introduce Targeted Video Denoising algorithm that extends the TID algorithm to denoise decompressed video without adding complexity. Our algorithm leverages the motion vectors generated during compression to establish temporal coherency between patches in consecutive frames. We test our algorithm on three decompressed video sequences with different foregrounds, backgrounds and movement patterns, and different noise level settings. Experimental results show that our approach is effective and performs better than original TID and the state-of-the-art video denoising algorithm.
Multi-Resolution Disparity Processing and Fusion for Large High-Resolution Stereo Image

Large panoramic views with high resolution have the advantage of a wide field of view over regular stereo views. However, the large size and high resolution impose difficulties on the stereo matching problem such as complexity and structure ambiguity, respectively. In this paper, effective multi-resolution disparity processing to resolve the difficulties is presented. We propose to adaptively determine the disparity search range based on the combined local structure from image intensity and initial disparity. The adaptive disparity range is able to propagate the smoothness property at low resolution to high resolution while preserving fine details. It reduces structure ambiguity as well as computational complexity. To reduce the disparity quantization error at the coarse level, we propose a reliable multiple fitting algorithm that is noticeably effective on the round surface. The spatial-multi-resolution total variation method is investigated to minimize inconsistency in space-scale dimension . The experimental results on the Middlebury datasets and real-world high-resolution images demonstrate that the proposed multi-resolution scheme produces high-quality and high-resolution disparity maps by fusing individual multi-scale disparity maps, while reducing complexity.
Multiview Glasses-Free 3-D Laparoscopy

Numerous attempts have been made to introduce three-dimensional (3-D) video systems into clinical routine, particularly for surgeries. The drawback with all of them thus far has been the fact that they require users to wear cumbersome glasses in order to receive the advantage of stereoscopy. In this study, we present, to our best knowledge, the world's very first laparoscopic surgical system that delivers glasses-free multiview 3-D in high-definition (HD) resolution. In addition to quantitative evaluations of the individual video processing components in our previous studies, we perform an initial subjective study with laparoscopically experienced surgeons, yielding very promising results. This study is still preliminary and requires further evaluation before the system may be introduced into standard surgical routine. Yet, we have developed a fully functioning prototype and successfully demonstrated its potential to numerous surgeons.
An investigation in dehazing compressed images and video

This paper proposes a novel method for single image dehazing that operates at a faster speed than current methods for implementation in video enhancements. We provide a comparison of our proposed dehazing method with current state of the art methods. We then consider the effect of compression by investigating the blocking and ringing artifacts in cases of applying any dehazing method before or after compression. Based on an investigation with the JPEG model, we conclude that the best dehazing performance (with less artifacts) is achieved if the dehazing is applied before compression. Simulations for both JPEG images and H.264 compressed sequences validate our conclusion.
Empirical Type-i filter design for image interpolation

Empirical filter designs generalize relationships inferred from training data to effect realistic solutions that conform well to the human visual system. Complex algorithms involving multiple linear regressions produce optimal results, but a single zero-phase filter yields comparable image quality at a fraction of the computational load. We propose an algorithm that builds a single symmetrical linear filter based purely on collected training data. Such a filtering technique balances the tradeoff between performance and complexity. Previous implementations of zero-phase interpolation filters as well as other learning-based interpolating algorithms are analyzed and examined. The proposed algorithm utilizes a Type-I symmetrical filter, an improvement and alternative over previous work on Type-II empirically-based interpolating filters. Given image training patches, the work discusses the enforcement of our filter properties while simultaneously drawing information from the training set. Additionally, we describe the implementation of the designed filter, its application, and related considerations. Finally, advantages of the proposed algorithm are analyzed.
Multirate processing of the ECG using filter banks

We propose an algorithm which can potentially perform multiple ECG processing tasks using a filter bank (FB). One set of filters in the FB decomposes the ECG into uniform frequency subbands. Since the subbands have a narrower bandwidth than the input ECG, they can be downsampled to get a lower rate subband signal. Time and frequency dependent processing can be performed at a lower rate and hence reduce the computation cost. A beat detection algorithm is presented which has minimal detection latency and good beat detection performance on the MIT/BIH database. The ECG can be enhanced by processing the subbands to remove noise. Features computed from the subbands can be used to distinguish between some ventricular and sinus beats. The FB offers a strategy to perform multiple tasks on the ECG using one set of filters operating at a computationally efficient rate.
Markov Random Field Model-Based Edge-Directed Image Interpolation

This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. Consequently, the local edge directions are represented by length-16 vectors, which are denoted as weight vectors. The weight vectors are used to formulate geometric regularity constraint, which is imposed on the interpolated image through the Markov Random Field (MRF) model. Furthermore, the interpolation problem is formulated as a Maximum A Posterior (MAP)-MRF problem and, under the MAP-MRF framework, the desired interpolated image corresponds to the minimal energy state of a two-dimensional random held. Simulated Annealing method is used to search for the minimal energy state from a reasonable large state space. Simulation and comparison results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity.
Some enhanced algorithms for robot navigation by omnidirectional cameras

To localize a point in a 3D scene, a previous approach used two omnidirectional cameras and combine with the Sum of Absolute Difference (SAD) to search for similar points. The old method gives low performance on repetitive textures. So an improvement for the SAD is proposed in this work. In the ego-motion estimation task, to take advantage of omnidirectional cameras, a new approach is presented: first, the Kanade-Lucas-Tomasi (KLT) feature tracker is improved for building a set of feature points, then the RANSAC algorithm is used to find the best consensus set from this, movement parameters are estimated in the next step by Newton-Gauss algorithm, and the final result is filtered again by Kalman filter. The improved KLT is robust against cameras' large rotation angles. By combining the proposed methods, a robot is able to reconstruct a 3D structure at a simple level. Experiments are performed using simulators: Google Sketchup and Pov-ray. Results show the new approach outperforms previous ones on problems that have been pointed out.
Handling consistency of software evolution in an efficient way

Software evolution by T. Katayama (2001) is about changes to software. A typical evolution scenario is to incorporate additional features into a system. In such a situation, there is a fundamental issue to be dealt with. It is to verify that the extra features do not violate existing properties adhered to the system, i.e. consistency. The verification method is also required to be efficient. This work focuses on the formal solution of these two issues. Systems are essentially structured in terms of features. Between features are interfaces through which features communicate with each other. The consistency between the system (base) and an extra feature (extension) during evolution is then verified via a modular model checking method by K. Fisler and S Krishnamurthi (2001). Instead of checking entirely the newly evolved system, the proposed method only executes in the extension with some base behavior assumption at the interface. This approach turns out to be very efficient as the verification complexity only depends on the extension. Further, the method is very flexible since the interface is open for unanticipated software changes. A case study is also provided to illustrate two eminent merits of the approach - efficiency in consistency verification and openness.