nils-herrmann/clean_scirepeval huggingface dataset:
Drop chain footgear: A new approach to environmentally friendly inshore Northern shrimp fishery in Newfoundland and Labrador, Canada

This study compared the effectiveness of a low seabed impact footgear versus a traditional rockhopper footgear on identical inshore Vónin shrimp trawls. The experimental trawl used in this study was designed to be low seabed impact through the reduction of contact area of the footgear by replacing traditional heavy rockhopper footgear with only a few drop chains lightly in contact with the seabed (i.e., drop chain footgear). Results from flume tank testing demonstrated that the trawls were similar in net geometry but the experimental trawl had a 62% reduction in contact area with the seabed compared to the control trawl. Comparative atsea fishing trials revealed that the drop chain footgear trawl has the potential to catch a similar amount of shrimp with appropriate drop chain rigging and modifications. Major by catch species (i.e., turbot, American plaice, and redfish) in terms of percentage of total catch in weight, quantities (i.e., counts), and size (i.e., length distribution) were not significantly different between trawl types. The experimental trawl had significantly lower resistance (i.e., warp tension) than the control trawl, however this did not translate into a detectable reduction in fuel consumption. Although the results are preliminary, this study demonstrated the potential and possibility of using drop chain footgear trawl for the Newfoundland and Labrador (inshore)Northern shrimp fishery. We recommended further studies that would lead to improve/optimize and eventually commercialize the drop chain footgear trawl as a low impact shrimp trawl choice in the province of Newfoundland and Labrador.
Image interpolation using classification and stitching

Image interpolation is a well-studied signal processing application that continues to receive substantial attention from the research community. It has been recognized that taking into account the presence of edges in an image can significantly improve the resulting interpolated image. Many techniques modify the interpolation method in the presence of edges to avoid common artifacts such as blurring, blocking and ringing. We propose to improve upon this idea by fusing the best features of several interpolation methods. This can be done using a novel region classification algorithm to determine which method is best suited to a particular region. From this information, we can use image mosaic techniques to fuse the methods into a result that contains both sharp edges and detailed textures.
A Novel Parametric Power Spectral Density Model for Images

This paper introduces a novel power spectral den- sity model for a class of images. This model is parametric with a variable number of parameters, and is easily defined in the correlation domain through the product of basic building blocks. By taking advantage of certain naturally occurring structural characteristics common to image power spectra, the result is capable of providing spectral detail that previous approaches could not. Consideration of these structural characteristics also allows accurate spectral estimates to be obtained even in the presence of some corruption and from low resolution data. That is, for typical images the model and estimation procedure provide a degree of robustness against some amounts of noise, distortion, and aliasing. The model and a numerical algorithm for estimating its parameters are presented along with some discussion and an example.
Lost transform domain data concealment in GenLOT based coders

In this paper, the problem of concealing lost transform domain data generated by a generalized lapped orthogonal transform (GenLOT) based image/video codec is considered. Based on the special structure of GenLOTs, we propose two computationally simple restoration algorithms one in the spatial domain, and the other in the transform domain, for the concealment of lost GenLOT coded data. Numeric and visual simulation results are provided for both algorithms along with their comparison to previous work.
Image noise estimation and removal considering the bayer pattern of noise variance

Traditional image denoising methods are designed for Gaussian or Poisson noise, which are not suitable for realistic noise introduced in the complicated imaging pipeline. We observe that, due to the demosaicing process in imaging, the noise variance maps of captured JPEG images are characterized by Bayer patterns. In this paper, we propose a novel noise estimation and removal method based on the Bayer pattern of noise variance maps. There are two key contributions in the proposed method. First, to the best of our knowledge, we are the first to consider the Bayer patterns of noise variance maps in noise estimation and denoising. Second, we extend the state-of-the-art denoising method CBM3D to deal with realistic noise by integrating the estimated noise variance map and Bayer-pattern down-sampling into the denoising process. Experimental results show that the proposed method achieves the best noise estimation performance compared with two state-of-the-art methods. In addition, the denoising performance of CBM3D for realistic noise is significantly improved using the proposed approach and outperforms state-of-the-art blind denoising methods.
2-D two-fold symmetric circular shaped filter design with homomorphic processing application

A design method of a linear-phased, two-dimensional (2-D), two-fold symmetric circular shaped filter is presented in this paper. Although the proposed method designs a non-separable filter, its implementation has linear complexity. The shape of the passband and the stopband is expressed in terms of level sets of second order trigonometric polynomials. This enables the transformation of the filter specifications to a Semi-Definite Program (SDP) of moderate dimension. The proposed filter outperforms currently available filter design methods. We present a performance comparison, as well as a homomorphic processing image enhancement example to illustrate the effectiveness of this method.
Path planning for a mobile robot in a dynamic environment

Nowadays, mobile robots work under the dynamic environments like manufacturing industries with machinery parts as moving objects and are using many techniques for navigation, obstacle avoidance, and localization. In this work we are using pioneer 2 DX mobile robot for experiments. This paper focuses on development of algorithms with the integration of path planning by potential field method and Monte Carlo localization method for navigation, obstacle avoidance, and localization of the mobile robot in a dynamic environment like in manufacturing industry. The path planning algorithms has divided into two submodules, one is global path planning which uses visibility graph with A* search method and another is local path planning which uses potential field method to avoid the obstacles. The image processing is used to get the working environment information from the global camera. The robot control program uses MCL algorithms with gradient path planning for continuous localization. User-friendly path planning software PMADE V 2.0 is developed. PMADE v 2.0 is used for image processing, path planning, simulation of robot navigation, real robot manual control and real robot automatic control for navigation in dynamic environment from position data of the simulator.
Motion Vector Processing Based on Residual Energy Information for Motion Compensated Frame Interpolation

In this paper, we address the problem of motion compensated frame interpolation (MCFI) at the decoder by analyzing received motion vectors (MVs) and residual error. In the proposed method, the skipped frames are generated at the decoder by using received information from the bitstream. We combine a hierarchical motion vector correction algorithm and a residual-energy constrained median filter to obtain true motion. Experimental results show that the proposed motion vector processing algorithm improves the visual quality of interpolated frames, especially in the motion transition area and the motion boundary.
A De-Interlacing Algorithm Using Markov Random Field Model

In this paper, a motion-compensated de-interlacing algorithm using the Markov random field (MRF) model is proposed. The de-interlacing problem is formulated as a maximum a posteriori (MAP) MRF problem. The MAP solution is the one that minimizes an energy function, which imposes discontinuity-adaptive smoothness (DAS) spatial constraint on the de-interlaced frame. The edge direction information, which is used to formulate the DAS constraint, is implicitly indicated by weight vectors (weights for 16 digitized directions). Generally, large weights are assigned to along-edge directions and relatively small weights are assigned to across-edge directions. As a local statistical-based method, the proposed weighting method should be more robust than traditional edge-directed interpolation methods in deciding local edge directions. The proposed algorithm is implemented by an iterative optimization process, which guarantees convergence. However, a global optimal solution is not guaranteed due to computational complexity concern. Simulation results compare the proposed algorithm to other motion compensated de-interlacing algorithms. Significant improvements of de-interlaced edges are observed.
Four-leaf clover-shaped antenna on an extended hemispherical lens for a high-output-power THz photomixer

A four-leaf-clover-shaped antenna composed of a highly resonant radiation element and a stable DC feed element and mounted on an extended hemispherical lens is described. The proposed antenna was optimized to have maximum total efficiency and maximum directivity on an extended hemispherical lens. Compared to a full-wavelength dipole antenna on a similar lens, we found a six-fold increase in the input resistance and an improvement in the total efficiency by a factor of 2.7. This is expected to reduce the impedance mismatch between the photomixer and antenna and lead to a significant increase in the THz output power.
Detecting temporally consistent objects in videos through object class label propagation

Object proposals for detecting moving or static video objects need to address issues such as speed, memory complexity and temporal consistency. We propose an efficient Video Object Proposal (VOP) generation method and show its efficacy in learning a better video object detector A deep-learning based video object detector learned using the proposed VOP achieves state-of-the-art detection performance on the Youtube-Objects dataset. We further propose a clustering of VOPs which can efficiently be used for detecting objects in video in a streaming fashion. As opposed to applying per-frame convolutional neural network (CNN) based object detection, our proposed method called Objects in Video Enabler thRough LAbel Propagation (OVERLAP) needs to classify only a small fraction of all candidate proposals in every video frame through streaming clustering of object proposals and class-label propagation. Source code for VOP clustering is available at https://github. com/subtri/streaming_VOP_clustering.
Pyramidal Lucas—Kanade-Based Noncontact Breath Motion Detection

This paper aims to build a simple and low-cost system by using images to detect human breath in a real-time fashion to estimate the peak of the inspiratory phase of a breath so as to define a proper triggering timing for X-ray shooting. In fact, it is very difficult to detect very small breathing motion on images. In this paper, well-known techniques are employed to obtain useful features from the chest area for the Lucas–Kanade algorithm. Various levels of the Pyramidal Lucas–Kanade are then adapted to track possible small motions of those features. The proposed approach can successfully detect the inspiratory–expiratory motions and the peak time of inspiratory phase can be predicted within an acceptable interval of error time. From the experiments conducted, the breath motion can be successfully observed in two different environment situations (dim-lighting and lighting conditions). It can be found that the tracked features are quite robust and stable without losing the quantity over a long period of testing time. Thus, the proposed approach can effectively be used to define a proper triggering timing for X-ray shooting. Besides, in our experiments, even though the target is 6 m away, the breath detection is still successful. In other words, the proposed approach can also be used for surveillance or healthcare environments.
Eigenfilters: A new approach to least-squares FIR filter design and applications including Nyquist filters

A new method of designing linear-phase FIR filters is proposed by minimizing a quadratic measure of the error in the passband and stopband. The method is based on the computation of an eigenvector of an appropriate real, symmetric, and positive-definite matrix. The proposed design procedure is general enough to incorporate both time- and frequency-domain constraints. For example, Nyquist filters can be easily designed using this approach. The design time for the new method is comparable to that of Remez exchange techniques. The passband and stopband errors in the frequency domain can be made equiripple by an iterative process, which involves feeding back the approximation error at each iteration. Several numerical design examples and comparisons to existing methods are presented, which demonstrate the usefulness of the present approach.
Wavelet-based image coder with entropy-constrained lattice vector quantizer (ECLVQ)

This paper presents an algorithm that jointly optimizes a lattice vector quantizer (LVQ) and an entropy coder in a subband coding at all ranges of bit rate. Estimation formulas for both entropy and distortion of lattice quantized subband images are derived. From these estimates, we then develop dynamic algorithm optimizing the LVQ and entropy coder together for a given entropy rate. Compared to previously reported min-max approaches, or approaches using asymptotic distortion bounds, the approach reported here quickly designs a highly accurate optimal entropy-constrained LVQ. The corresponding wavelet-based image coder also has better coding performance compared to other subband coders that use entropy-constrained LVQ, especially at low bit rates.
Spatio-temporal consistency in video disparity estimation

We present a novel stereo video disparity estimation method. The proposed method is a two-stage algorithm. During the first stage, initial disparity maps are computed in a frame-by-frame basis. In the second stage, the initial estimates are treated as a space-time volume. By setting up an l1-normed minimization problem with a novel three-dimensional total variation regularization, spatial smoothness and temporal consistency are handled simultaneously. Due to our unique formulation, any existing image disparity estimation technique may utilize our method as a post-processing step to refine noisy estimates or to be extended to videos. The proposed method shows superior speed, accuracy, and consistency compared to state-of-the-art algorithms.
Edge-based directional fuzzy filter for compression artifact reduction in JPEG images

We propose a novel method to reduce both blocking and ringing artifacts in compressed images and videos. Based on the directional characteristics of ringing artifacts along edges, we use a directional fuzzy filter which is adaptive to the direction of the ringing artifact. The filter exploits the spatial order, the rank order and the spread information of the signal together with the position of the pixels to enhance the quality of the compressed image. Simulations results on compressed images and videos having simple and complex edges show the improvement of the proposed directional fuzzy filter over the conventional fuzzy filtering and other approaches.
A Class of Multiresolution Directional Filter Banks

In this paper, we introduced a class of directional filter banks (DFBs) having the previously proposed uniform DFB (uDFB) as a special case. Except for the uDFB, each DFB in this class can be used to decompose an image yielding up to 12 directions while maintaining perfect reconstruction and maximal decimation. A multiresolution representation can be obtained by repeating the same decomposition at the lowpass band. The permissible property of the filter banks in cases of being implemented by a tree structure and by direct implementation is discussed. The result shows that only one DFB in the class, called the uniform quincunx DFB (uqDFB), satisfies the permissible property when being implemented directly without using the tree structure. The nonuniform quincunx DFB (nuqDFB) is then constructed from the uqDFB by merging its two lowpass subbands. An alternative structure for constructing the nuqDFB is presented. The new structure, while yielding the same frequency partitioning, allows the DFB to be realized with complexity comparable to that of the separable wavelet filter bank. The connection between the discrete filter bank and the continuous directional wavelet is also established. Numerical experiments on directional feature extractions, image denoising and nonlinear approximation are presented at the end of the paper to demonstrate the potential of the nuqDFB
Linear phase orthonormal filter banks

Paraunitary systems in which each individual filter in the analysis and synthesis banks has linear phase are studied. This property is often desirable for several applications, particularly in image processing. Several theoretical questions pertaining to linear phase paraunitary systems are answered. Next, a factorization for such systems is developed which is proved to be minimal as well as complete. 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 presented.<<ETX>>
Learning the properties of Receptive Fields in the context of Perceptual Image Quality assessment

In this paper we introduce a statistical framework for image quality assessment based on the properties of hierarchical Receptive Fields (RFs) which are the primary mechanism for detection of visual patterns in the Human Visual System (HVS). We show how this frame work can be used to learn about different aspects of RFs such as the shape and size of RFs in the early vision and the directional preference of the RFs in the V1 cortex. The proposed framework offers a probabilistic approach to the detection of discrepancies (distortion) between a reference and a test visual stimuli (e.g. images). The proposed Probabilistic Perceptual Image Quality (PPIQ) framework offers a more realistic notion of image quality assessment, based on “comparative memory” as opposed to “differential photographic memory”, which was required for explanation of many aspects of legacy image quality methods.
A low-cost high-speed source-synchronous interconnection technique for GALS chip multiprocessors

The globally asynchronous locally synchronous (GALS) design style for a large area chip has become increasingly attractive due to the difficulty of designing global clocking circuits at high clock frequencies in the GHz range. In this paper, we present a high-speed interconnect network for a GALS multiprocessing system composed of a 2-D mesh array of processors. Processors are locally clocked by their own oscillators and communicate together using a static circuit-switched technique combined with a source-synchronous communication scheme. A technique to maximize the timing reliability on long-distance interconnects at high clock rates is proposed that is area and power efficient with low latency and allows a sustained ideal peak throughput of one word per cycle.
Optimal triplet halfband filter banks with k-regularity and image coding application

The triplet halfband filter bank structure is well known to be efficient for the design and implementation of a class of biorthogonal wavelet filter banks. Previously, two extreme cases in filter characteristics including equiripple filters with no regularity and maximally flat filters with poor frequency selectivity have mainly been treated. This paper proposes an efficient semi-definite programming (SDP) method for the design of linear phase finite impulse response (FIR) triplet halfband filter banks whose filters have optimal frequency selectivity for a prescribed regularity order. By using the linear matrix inequality (LMI) characterization of the trigonometric semi-infinite constraints, the design problem can be exactly cast as an SDP problem with a small number of variables and, hence, can be solved efficiently. A design example of the triplet halfband filter bank with different regularity orders is provided to validate the proposed method. Finally, the image coding performance of the filter bank is presented.
Filter bank-based ECG beat classification

A multirate digital signal processing algorithm to classify heart beats in the electrocardiogram (ECG) is presented. 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 the ECG. Various features are computed from the subbands to distinguish between between paced and non-paced beats. These features are computed in a very computationally efficient manner. A machine learning method is used to design a decision tree to be used in the classification process. The classification algorithms were tested on records from the MIT/BIH database. The paced beat classification algorithm has a sensitivity of 87.64% and a positive predictivity of 90.97%. The FB-based structure is useful for performing multiple ECG processing tasks using one set of preprocessing filters.
Sharp disparity reconstruction using sparse disparity measurement and color information

Recently, the work on dense disparity map reconstruction from 5% sparse initial estimates containing edges in disparity, has been proposed . Practically, however, edges in disparity is unknown unless a dense disparity map has already been generated. In this paper, we present a realistic reconstruction framework for obtaining sharp and dense disparity maps from fixed number of sparse initial estimates with the aid of color image information. Experimental results show that sharp and dense disparity maps can be reconstructed at the cost of one pixel accuracy.
The design of a reconfigurable continuous-flow mixed-radix FFT processor

The design of a highly configurable continuous flow mixed-radix (CFMR) Fast Fourier Transform (FFT) processor is presented. It computes fixed-point complex FFTs and inverse FFTs (IFFTs), and utilizes a flexible addressing scheme to enable runtime configuration of the FFT length from 16-points to 4096-points. A configurable block floating point (BFP) unit increases numerical performance. Compared to a floating point Matlab FFT function, the accuracy of the proposed architecture is 80 dB for a 64-point FFT and 74 dB for a 1024-point FFT with random complex input data.
Analysis of Utility Functions for Video

In this paper, we formulate the utility functions of distortion and peak signal-to-noise ratio (PSNR) which are generally used for the performance evaluation of video coding applications as the utility functions. The convexity property of these utility functions is analyzed in the original domain and transformed domain of an optimization variable. From this analysis, we derive joint optimization scheme with congestion control through the utility matching between TCP layer and video coding layer. Experimental results show that the overall PSNR increases and the variation of quality among the utility functions is reduced.