nils-herrmann/clean_scirepeval huggingface dataset:
Gossip-Based Cooperative Caching for Mobile Phone Games in IMANETs

Cooperative caching is an efficient way to improve the performance of data access in mobile wireless networks, by cache nodes cooperatively caching copies of data items in their limited storage. With more demands on sharing a video or other data, especially for mobile phone games in an Internet-based Mobile Ad Hoc Network, the relations among data items become much more important than before. However, most of the existing works do not consider inherent relations among these data items. In this paper, we present a novel solution, Gossip-based Cooperative Caching (GosCC), to address the cache placement problem, considering the sequential relation among data items. Each mobile node stores the IDs of data items cached locally and the ID of the data item in use into its progress report. Each mobile node also makes use of these progress reports to determine whether a data item should be cached locally. These progress reports are propagated within the network in a gossip-based way. To improve user experience, GosCC aims to provide users with an uninterrupted data access service. Simulation results show that GosCC achieves better performance than the best one of the existing solutions, Benefit-based Data Caching, in terms of average interruption intervals and average interruption times, while sacrificing message cost to a certain degree.
ER-CRLB: An Extended Recursive Cramér–Rao Lower Bound Fundamental Analysis Method for Indoor Localization Systems

We propose an extended recursive Cramér–Rao lower bound (ER-CRLB) method as a fundamental tool to analyze the performance of wireless indoor localization systems. The ER-CRLB fully models the complicated indoor environment, e.g., the sequential position state propagation, the target-anchor geometry effect, the non-line-of-sight (NLOS) identification, and the related prior information. First, we use an abstract function to represent the entire wireless localization system model. Then, the unknown vector of the ER-CRLB consists of two parts: The first part is the estimated vector, and the second part is the auxiliary vector that helps improve the estimation accuracy. Accordingly, the Fisher information matrix (FIM) of the ER-CRLB is divided into two parts, namely, the state matrix and the auxiliary matrix. Based on this idea, ER-CRLB can be a practical fundamental limit to denote the system which fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and NLOS identification and mitigation methods. When only a small set of unknown vectors is estimated in the system, the ER-CRLB is equivalent to the other CRLBs of the wireless indoor localization system as well. However, the ER-CRLB is more adaptable than other CRLBs when considering more unknown important factors. We employ the ER-CRLB to analyze the time-of-arrival (TOA) range-based indoor localization system. The influence of the hybrid line-of-sight (LOS)/NLOS channels, the building layout information, and the relative height differences between the target and anchors are analyzed. It is demonstrated that the ER-CRLB exploits all the available information for the indoor localization systems and serves as a fundamental limit of the unbiased estimation accuracy.
The ClearPET/XPAD prototype: Development of a simultaneous PET/CT scanner for mice

We developed the ClearPET/XPAD prototype: the first simultaneous PET/CT mouse scanner combining on the same rotating gantry ClearPET modules and the hybrid pixel camera XPAD3 facing an X-ray source. To demonstrate the capabilities of the new machine, we present the first simultaneous PET/CT scans of 68Ge and 22Na point sources, of a micro Derenzo phantom and of a live mouse injected in the tail vein with [18F]FDG.
Development of K-edge spectral tomography using XPAD3 composite pixels

Hybrid pixel cameras are new devices in which photon counting replaces charge integration used in standard radiography systems. This novel approach involves several advantages, in particular the absence of dark noise integration and the ability to set an energy threshold on each pixel of the detected photons. This ability is of uppermost importance for the development of K-edge imaging approaches, which exploit spectral information on the counted photons. We investigate the capability to perform K-edge imaging by using the hybrid pixel camera XPAD3 with 500,000 pixels of (130 × 130) μm2 integrated in the micro-CT demonstrator PIXSCAN II. K-edge images are obtained from three sets of projections acquired from the same subject under three different energy threshold conditions around the K-shell binding energy of a contrast agent. To minimize the time dependent changes of the biological samples between the three acquisitions, we have developed a composite pixel protocol allowing simultaneous acquisitions of the projections for three different energy thresholds. This methodology reduces the acquisition duration by a factor three at the expense of a corresponding loss of spatial resolution. We nevertheless demonstrate the validity of the method on different phantoms and finally on mice.
Comparison of the performance of the photon counting hybrid pixel camera XPAD3 versus the CCD camera DALSA XR-4 for cone-beam micro-CT

Hybrid pixel detectors represent a new generation of digital X-ray camera working in a photon counting mode that can replace conventional charge integration in 2D detectors. We compare cone-beam CT using either the hybrid pixel camera XPAD3 or the CCD camera DALSA XR-4 within the newly developed micro-CT irradiation setup PIXSCAN within a range of total integrated doses from 30 to 900 mGy. Detector stability, image uniformity, spatial resolution, noise power spectrum, and detectability at low contrast were studied for both cameras. The results we obtained confirmed the absence of dark current for the hybrid pixel photon counting camera XPAD3, although the two detectors are virtually equivalent in standard conditions. The linearity of the CT value is demonstrated for both cameras, but the achieved contrast and spatial resolution are noticeably better with the hybrid pixel camera than with the CCD camera.
Hybrid GATE: A GPU/CPU implementation for imaging and therapy applications

Monte Carlo simulations (MCS) play a key role in medical applications. In this context GATE is a MCS platform dedicated to medical imaging and particle therapy. Yet MCS are very computationally demanding, which limits their applicability in clinical practice. Recently, graphics processing units (GPU) became, in many domains, a cost-effective solution to access high power computation. The objective of this work was to develop a GPU code targeting MCS for medical applications integrated within the GATE software. An aim was to enhance GATE computational efficiency by taking advantage of GPU architectures. We first developed a GPU framework with basic elements to run MCS for different medical applications. The implementation was based on a GPU adaptation of the Geant4 code. For each main GATE medical application, we developed a specific code from the GPU framework. Some of these GPU codes are currently being integrated in GATE as new features, and users can perform GPU computing in their GATE simulations. The acceleration factor resulting from the implementation of the tracking within the phantom on GPU was 60 for a PET simulation and 80 for a CT simulation. By using GPU architectures, we are also extending GATE to support optical imaging simulations that are heavily demanding in terms of computational resources. Radiation therapy applications currently supported by GATE V6.2 are also being adapted to run on hybrid GPU/CPU architectures.
Implementing Geant4 on GPU for medical applications

Monte Carlo simulation (MCS) plays a key role in medical applications, especially for emission tomography (ET) and radiotherapy (RT). Unfortunately MCS is also associated with long calculation times that prevent for using it in routine clinical practice. Actually, a solution based on the use of computer clusters to solve the intensive computational issues is not realistic within routine clinical environment. Recently graphics processing units (GPU) became in many domains a cheap solution for the acquisition of a high power computation. The objective of this work was to develop an efficient framework for the implementation of MCS on GPU architectures. Geant4 was used as the MCS engine for targeting medical imaging and radiotherapy applications. We propose the definition of a global strategy and associated structures for such a GPU based simulation. The different steps needed for a Geant4 simulation were implemented on GPU. The first validations have shown equivalence in the underlying photon physics processes between the Geant4 and the GPU codes. Based on these simplistic simulations, we are expecting a speedup factor of over 200 for a complete simulation in emission tomography or in radiotherapy dosimetry.
Simultaneous reconstruction and separation in a spectral CT framework

In this paper, we propose a method to simultaneously separate and reconstruct the physical components of an object observed in the context of spectral Computed Tomography (CT). Spectral CT has been made possible thansk to the technological breakthrough of X-ray hybrid pixel detectors in the last years and brings CT imaging from an anatomic to a functional biomedical imaging modality. Our contribution considers the underlying polychromatic model of the X-ray beam and combines it with a low-rank prior on the components of the object to reconstruct. The simultaneous separation and reconstruction of components is done by solving a non-convex ill-posed inverse problem. Promising results are shown on synthetic data with a simple regularization that encompasses the positivity of quantities of interest.
On the efficiency of proximal methods in CBCT and PET

Cone Beam Computerized Tomography (CBCT) and Positron Emission Tomography (PET) Scans are medical imaging devices that require solving ill-posed inverse problems. The models considered come directly from the physics of the acquisition devices, and take into account the specificity of the (Poisson) noise. We propose various fast numerical schemes to compute the solution. In particular, we show that a new algorithm recently introduced by A. Chambolle and T. Pock is well suited in the PET case when considering non differentiable regularizations such as total variation or wavelet ℓ1-regularization. Numerical experiments indicate that the proposed algorithms compare favorably with respect to well-established methods in tomography.
First K-Edge Imaging With a Micro-CT Based on the XPAD3 Hybrid Pixel Detector

We investigate the capability to perform K-edge imaging with the newly developed micro-CT PIXSCAN based on the XPAD3 hybrid pixel detector. The XPAD3 detector surface of 8 cm ×11 cm makes it possible to perform whole body mouse imaging. We present a proof of principle of K-edge imaging of mouse-size phantoms filled with Silver and Iodine solutions. Results are compared with standard X-ray absorption tomography for several solution densities.
Address predictive color quantization image compression for multimedia applications

Most modem workstations and personal computers are using 8-bit color mapped graphics architecture for displaying color images. To display a decompressed color image of the conventional DCT-based compression algorithms on these palette-based display systems, the decoded image must first be color quantized. To avoid the computational intensive color quantization process and provide a fast decoding process, new address predictive color quantization image compression schemes for multimedia applications are proposed in this paper. A closest pairs color palette ordering technique is also proposed for effectively exploited the redundancy of the palettized image. Computer simulation results are given to show the performance of the new compression algorithms in terms of bit rate and signal to noise ratio.<<ETX>>
Generalized partial distortion search algorithm for fast block motion estimation

Quality versus speed control for real-time video applications, such as speed-oriented videoconferencing or high quality video entertainment, usually is absent from many traditional fast block motion estimators. A novel block-matching algorithm for fast motion estimation named generalized partial distortion search algorithm (GPDS) is proposed. It uses a halfway-stop technique with progressive partial distortion (PPD) to increase the chance of early rejection of impossible candidate motion vectors at very early stages. Simulations on PPD show 28 to 38 times computational reduction with only 0.45-0.50 dB PSNR performance degradation as compared to the full search algorithm. In addition, a new normalized partial distortion comparison method is also proposed for enabling control of searching speed against prediction quality by a speedup factor k. This method also generalizes the conventional partial distortion search algorithm when k is equal to 1, and the normalized partial distortion search algorithm (NPDS) when k is equal to infinity. Experimental results show that GPDS with use of PPD could provide PSNR performance very close to the full search algorithm and NPDS with 7 to 17 times and 22 to 33 times speedup, respectively, as compared to the full search algorithm.
Minimax partial distortion competitive learning for optimal codebook design

The design of the optimal codebook for a given codebook size and input source is a challenging puzzle that remains to be solved. The key problem in optimal codebook design is how to construct a set of codevectors efficiently to minimize the average distortion. A minimax criterion of minimizing the maximum partial distortion is introduced in this paper. Based on the partial distortion theorem, it is shown that minimizing the maximum partial distortion and minimizing the average distortion will asymptotically have the same optimal solution corresponding to equal and minimal partial distortion. Motivated by the result, we incorporate the alternative minimax criterion into the on-line learning mechanism, and develop a new algorithm called minimax partial distortion competitive learning (MMPDCL) for optimal codebook design. A computation acceleration scheme for the MMPDCL algorithm is implemented using the partial distance search technique, thus significantly increasing its computational efficiency. Extensive experiments have demonstrated that compared with some well-known codebook design algorithms, the MMPDCL algorithm consistently produces the best codebooks with the smallest average distortions. As the codebook size increases, the performance gain becomes more significant using the MMPDCL algorithm. The robustness and computational efficiency of this new algorithm further highlight its advantages.
Novel Point-Oriented Inner Searches for Fast Block Motion Estimation

Recently, an enhanced hexagon-based (EHS) search algorithm was proposed to speedup the original hexagon-based search (HS) using a 6-side-based fast inner search. However, this 6-side-based method is quite irregular by inspecting the distance between the inner search points and the coarse search points that would lower prediction accuracy. In this paper, a new point-oriented grouping strategy is proposed to develop fast inner search techniques for speeding up the HS and diamond search (DS) algorithms. Experimental results show that the new HS and DS using point-oriented inner searches are faster than their original algorithms up to 30% with negligible peak signal-to-noise ratio degradation
A novel watermarking scheme with compensation in bit-stream domain for H.264/AVC

Currently, most of the watermarking algorithms for H.264/AVC video coding standard are encoder-based due to their high perceptual quality. However, for the compressed video, they increase the computational burden to decode the video, embed the watermark, and then re-encode it. Obviously it is a bottleneck for real-time applications. Conventional watermarking algorithms in the bit-stream domain can reduce the computation complexity but result in the error propagation and PSNR loss. In this paper, a new fast watermarking scheme with compensation in bit-stream compressed video domain is proposed, in which the watermark is directly embedded into the quantized residual coefficients. A texture-based perceptual model is employed to decide whether a 4×4-block is appropriate for watermarking or not. A secret key is used to decide the actual location to be embedded in a 4×4-block. With the proposed compensation method, the video watermarking scheme can achieve high robustness and good visual quality without much bit-rate increase. The simulation results have shown that a high PSNR is achieved with little increase in bit rate, and the watermarks can resist common attacks.
Fast block motion estimation with early acceptance technique in H.264/JVT

MPEG-4 Visual (Part 10) or H.264, also known as advanced video coding (AVC), is the newest standard developed by the joint video team (JVT) for the coded representation (or syntax) of video. In this paper we first present an overview of the state-of-the-art video compression standard - H.264/AVC. Then we give a performance evaluation of our proposed KCDS and EAPDS algorithms on H.264 encoder by comparing them with other algorithms. In the final section, we combine our algorithms with a motion vector prediction technique and perform an experiment to have a performance evaluation with H.264 embedded fast BMA, which is UMHexgonS.
Transform-Domain Fast Sum of the Squared Difference Computation for H.264/AVC Rate-Distortion Optimization

In H.264/AVC, the rate-distortion optimization for mode decision plays a significant role to achieve its outstanding performance in terms of both compression efficiency and video quality. However, this mode decision process also introduces extremely high complexity in the encoding process especially the computation of the sum of squared differences (SSD) between the original and reconstructed image blocks. In this paper, fast SSD (FSSD) algorithms are proposed to reduce the complexity of the rate-distortion cost function implementation. The proposed FSSD algorithm is based on the theoretical equivalent of the SSDs in spatial and transform domains and determines the distortion in integer cosine transform domain using an iterative table-lookup quantization process. This approach could avoid the inverse quantization/transform and pixel reconstructions processes with nearly no rate-distortion performance degradation. In addition, the FSSD can also be used with efficient bit rate estimation algorithms to further reduce the cost function complexity. Experimental results show that the new FSSD can save up to 15% of total encoding time with less than 0.1% coding performance degradation and it can save up to 30% with ignorable performance degradation when combining with conventional bit rate estimation algorithm
Merged palette histogram relevance feedback for MPEG-7 dominant color descriptor

A relevance feedback (RF) technique for improving the effectiveness of content based image retrieval using MPEG-7 dominant color descriptor (DCD) is proposed in this paper. It uses a merged palette histogram (MPH) technique for generating a new query DCD histogram from the relevant image set. The MPH approach can generate a natural query that can represent the selected relevance images. The new method has been implemented on a MPEG-7 XM platform with use of the MPEG-7 common color dataset. The effectiveness of this new MPH-RF technique using different similarity measures is demonstrated by experimental results in the terms of averaged normalized modified retrieval rate (ANMRR), computational analysis and visual retrieved images comparison.
Watershed based depth map misalignment correction and foreground biased dilation for DIBR view synthesis

The quality of the synthesized views by Depth Image Based Rendering (DIBR) highly depends on the accuracy of the depth map, especially the alignment of object boundaries of texture image. In practice, the misalignment of sharp depth map edges is the major cause of the annoying artifacts at the disoccluded regions of the synthesized views. In this paper, a new depth map preprocessing method using Watershed misalignment correction and dilation filter is proposed to align the foreground depth edges to cover the whole transitional color edge regions. This approach can handle the sharp depth map edges lying inside or outside the object boundaries in 2D sense. The quality of the disoccluded regions of the synthesized views can be significantly improved. Experimental results show that the proposed method achieves superior performance for view synthesis by DIBR especially for generating large baseline virtual views.
Deep learning for face anti-spoofing: An end-to-end approach

The importance of face anti-spoofing algorithms in biometric authentication systems is becoming indispensable. Recently, the success of Convolution Neural Networks (CNN) in key application areas of computer vision has encouraged its use in face biometrics for face anti-spoofing and verification applications. However, small training data has restricted the use of deep CNN architectures for face anti-spoofing applications. In this paper, we develop an end-to-end CNN architecture for face anti-spoofing application. i.e. a deep CNN architecture which directly map the raw input face images to the corresponding output classes. Additionally, an efficient training strategy has been proposed to enable the use of deeper CNN structures for face anti-spoofing applications and to enable the growth of training data in autonomous way. For training a CNN architecture, we propose a 50RS-30SeC-1E (50 Random Samples-30 Sub-epochs Count-1Epoch) training strategy. The training data is randomly sampled during each forward-pass through the CNN architecture and 30 such passes counts for 1 complete epoch. An 11-layer VGG network with 2 derived VGG-11 networks have been trained for face anti-spoofing on CASIA-FASD dataset. Experimental results show significant improvement on various face-spoofing scenarios. A 3% improvement over state of the art approaches has been reported for Overall Test (OT) while achieving a lowest EER of 5%.
A complexity reduction technique for image vector quantization

A technique for reducing the complexity of spatial-domain image vector quantization (VQ) is proposed. The conventional spatial domain distortion measure is replaced by a transform domain subspace distortion measure. Due to the energy compaction properties of image transforms, the dimensionality of the subspace distortion measure can be reduced drastically without significantly affecting the performance of the new quantizer. A modified LBG algorithm incorporating the new distortion measure is proposed. Unlike conventional transform domain VQ, the codevector dimension is not reduced and a better image quality is guaranteed. The performance and design considerations of a real-time image encoder using the techniques are investigated. Compared with spatial domain a speed up in both codebook design time and search time is obtained for mean residual VQ, and the size of fast RAM is reduced by a factor of four. Degradation of image quality is less than 0.4 dB in PSNR.