nils-herrmann/clean_scirepeval huggingface dataset:
WLCp2-02: Differentiated Multiclass QoS in Wireless Multicarrier Environments

Differentiated QoS provision in wireless environments via multicarrier energy allocation is examined in this work. Within this framework, differentiated QoS is applied to transmitted data classes, such as text, audio and video, simultaneously. QoS targets, or constraints, are defined by specifying distortion targets for each class, and energy is allocated to carriers of different classes to meet those targets/constraints accordingly. QoS differentiation is achieved by a concept called QoS scaling. We develop a methodology to find the set of scalars that allows each service constraint to be met. While earlier work can be viewed to offer solutions to the scalar-to-distortion mapping problem, this research solves the more challenging inverse mapping problem; namely, from distortion requirements to selection of scalars. We present two algorithms for differentiated QoS that depend upon the amount of constrained classes relative to the total amount of classes. Finally, to provide a clearer understanding of the effect of QoS scaling, we analyze the effect on distortion by varying the total number of scalars.
Decoder-Friendly Adaptive Deblocking Filter (DF-ADF) Mode Decision in H.264/AVC

Video encoding to yield a decoder-friendly H.264 bit stream that consumes less decoding power yet with little coding efficiency degradation is investigated in this work. The energy saving of the decoder relies on the use of adaptive deblocking filters (ADF). We first propose a power consumption model for the deblocking filter. Then, the encoder performs the rate-distortion-decoder complexity optimization (RDC) to save the decoder power needed for deblocking filter operations, which is called the decoder-friendly adaptive deblocking filter (DF-ADF) mode decision. The RDC optimization framework presents a way to balance coding efficiency and the ADF decoding cost in the mode decision process. The effectiveness of the proposed DF-ADF algorithm is demonstrated by experiments with diverse video contents and bit rates.
Discrete Power Allocation for Lifetime Maximization in Cooperative Networks

Discrete power allocation strategies for amplify- and-forward cooperative networks are proposed based on selective relaying methods. The goal of power allocation is to maximize the network lifetime, which is defined as the duration of time for which the outage probability at the destination can be maintained above a certain level. The discrete power levels enable a low cost implementation and a close integration with high speed digital circuits. We propose three power allocation strategies that take into consideration both the channel state information (CSI) and the residual energy information (REI) at each node. By modeling the residual energy of each node as the states of a Markov Chain, we are able to derive the network lifetime analytically by computing the expected number of transitions to the absorbing states, i.e., the energy states for which the outage probability is no longer achievable. The performance of the three strategies are compared through numerical simulations and a significant improvement in network lifetime is shown, when compared with the case considering only the local CSI.
Robust coding of 3D graphic models using mesh segmentation and data partitioning

Current coding techniques for 3D graphic models focus more on coding efficiency, which makes them extremely sensitive to channel errors due to the irregular nature of the mesh structure. In this paper, we propose a robust mesh partitioning scheme which can reduce the error propagation length and allow a piecewise reconstruction of the original mesh while maintaining a high compression ratio. Our method first segments an arbitrary 3D mesh into a set of independent connected components. Each component is further segmented into several independent 3D pieces based on the error rate. Joint boundary information is efficiently coded which will be used to stick segmented pieces together. All resulting pieces are grouped by their sizes to keep each group as a basic unit which has a near equal length, which is determined by the error rate. The data partitioning technique is finally used to keep track of each group of pieces. Errors are thus limited to each group, and can be corrected easily. The proposed technique can also be used in graphic editing, partial texture mapping and other applications.
Embedded space-time coding for wireless broadcast with heterogeneous receivers

An embedded space-time coding method is proposed for wireless broadcast with heterogeneous receivers. In the proposed system, a transmitter sends out multi-layer source signals by encoding different layers with different space-time codes. Then, a receiver can retrieve a different amount of information depending on the number of its antennas. The receiver with only one antenna can decode only the base layer information with a low complexity, while the receiver with more antennas can retrieve more layers of information. We also investigate a differential design of the embedded space-time system, which is used when the channel state information is unknown to the receiver. Both analytic and experimental results are provided to demonstrate the performance of the proposed system.
An Approximately MAI-Free Multiaccess OFDM System in Fast Time-Varying Channels

A new multiuser orthogonal frequency division multiplexing (OFDM) transceiver with preceding, called the precoded multiuser OFDM (PMU-OFDM) system, was recently introduced by Tsai et al. (2005). The PMU-OFDM system can reduce multiaccess interference (MAI) due to the carrier frequency offset (CFO) to a negligible amount by preceding the data of each user with a codeword selected from either even or odd Hadamard-Walsh codes. The performance of the PMU-OFDM system in a mobile environment, where the channel response varies within one OFDM symbol due to the Doppler effect, is evaluated in this paper. It is shown by analysis and simulation that the use of even or odd Hadamard-Walsh codewords can greatly reduce the MAI effect due to the Doppler effect as well. Generally, all Hadamard-Walsh codewords except for the all-one codeword result in considerable suppression of intercarrier interference (ICI) induced by the Doppler effect. Furthermore, we discuss codeword priority schemes to minimize ICI as much as possible. Finally, simulations results are given to demonstrate the advantage of PMU-OFDM over OFDMA with a codeword priority scheme in the Doppler environment. We show that the PMU-OFDM system with odd Hadamard-Walsh codewords outperforms a half-loaded OFDMA system in a mobile environment.
Lifetime Maximization for Amplify-and-Forward Cooperative Networks

Power allocation strategies are devised to maximize the network lifetime of amplify-and-forward (AF) cooperative networks. The paper considers the scenario where one source and multiple partners cooperate to transmit messages to the destination. The powers emitted by the users are subject to the SNR requirement at the destination. First, the power allocation strategy that demands the minimum instantaneous aggregate transmit power of all cooperating partners is described and analyzed. The optimal solution results in a form of selective relaying; namely, the user with the best channel condition is selected to help in relaying the message. However, this instantaneous power minimization strategy does not necessarily maximize the lifetime of battery-limited systems. Then, three AF cooperative schemes were proposed to exploit the channel state information (CSI), the residual battery energy and the QoS requirement. It is shown that the network lifetime can be extended considerably by taking all these three factors into account.
Low complexity algorithm for robust video frame rate up-conversion (FRUC) technique

Two challenging situations for video frame rate up-conversion (FRUC) are first identified and analyzed; namely, when the input video has abrupt illumination change and/or a low frame rate. Then, a low-complexity processing technique and robust FRUC algorithm are proposed to address these two issues. The proposed algorithm utilizes a translational motion vector model of the first- and the second-order and detects the continuity of these motion vectors. Additionally, in order to improve perceptual quality of interpolated frame, spatial smoothness criterion is employed. The superior performance of the proposed algorithm has been tested extensively and representative examples are given in this work.
Similar Segment Detection for Music Structure Analysis via Viterbi Algorithm

The analysis of audio signals of popular and rock songs of the verse-chorus form to reconstruct its original musical structures is investigated in this work. We first compute the similarity degree between any two measures in a song based on selected features and represent these numbers in a measure-based similarity matrix. Then, we study the similarity across a sequence of consecutive measures, which is revealed by straight segments in parallel with the diagonal line of the similarity matrix. Generally, chorus parts have higher similarity values while verse parts have lower similarity values. As a result, the verse parts are difficult to detect in the presence of the chorus parts. To tackle this problem systematically, the Viterbi algorithm is adopted to find optimal paths in the lower-triangular similarity matrix, which represent repetitive segments of both choruses and verses. Finally, several post-processing steps are developed to decode the music structure into the verse, the chorus and other non-repetitive parts. Experimental results obtained from several musical audio data are shown to demonstrate the performance of the proposed method
Rate-distortion optimized compression and view-dependent transmission of 3-D normal meshes

A unified approach to rate-distortion (R-D) optimized compression and view-dependent transmission of three-dimensional (3-D) normal meshes is investigated in this work. A normal mesh is partitioned into several segments, which are then encoded independently. The bitstream of each segment is truncated optimally using a geometry distortion model based on the subdivision hierarchy. It is shown that the proposed compression algorithm yields a higher coding gain than the conventional algorithm. Moreover, to facilitate interactive transmission of 3-D data according to a client's viewing position, the server can allocate an adaptive bitrate to each segment based on its visibility priority. Simulation results demonstrate that the view-dependent transmission technique can reduce the bandwidth requirement considerably, while maintaining a good visual quality.
Performance Analysis of Single-User Ultra-Wide Band Impulse Radio (UWB-IR) with Super-Orthogonal Turbo Codes (SOTC)

Low-complexity low-rate super-orthogonal turbo codes (SOTC) are proposed in this work to replace the implicit repetition code (RC) in ultra-wide band impulse radio (UWB-IR) systems to improve the transmission range and system throughput. Various receivers, including the matched-filter and the RAKE receivers, are examined for data detection in the additive white Gaussian noise (AWGN) channel and the indoor IEEE 802.15.3a channels. The performance of SOTC-coded UWB with perfect and imperfect timing and channel information is analyzed and corroborated by computer simulation. It is demonstrated that the SOTC-based UWB-IR system can achieve significant performance improvement over the conventional direct-sequence UWB (DS-UWB) system encoded by RC over both ISI and ISI-free channels.
A precoded multiuser OFDM (PMU-OFDM) transceiver for time asynchronous systems

The performance of a multiuser OFDM transceiver, proposed by authors in S.H. Tsai, et al. (2004) and called the preceded multiuser OFDM (PMU-OFDM) in this work, with time asynchronous access is investigated. We show that the multiaccess interference (MAI) due to time asynchronous access can be reduced to a negligible amount by adopting a proper precoder that uses M/2 symmetric or M/2 anti-symmetric codewords of the M Hadamard-Walsh codes for the preceding task. The PMU-OFDM transceiver with the same precoder was shown to have approximately MAI-free property in a frequency asynchronous environment (S.H. Tsai, et al., 2005). Here, we analyze the approximately MAI-free property of the PMU-OFDM system with respect to time asynchronism. Finally, computer simulation is performed to confirm the derived property
Real-time H.263+ frame rate control for low bit rate VBR video

H.263+ is an emerging video compression standard for the low bit rate visual communication. Most H.263+ rate control algorithms, e.g. the one used in the test model of the near-term (TMN), focus on macroblock level bit allocation and low latency by assuming a constant frame rate video encoding sent through a constant bit rate (CBR) channel. These algorithms cannot handle the fluctuation of the channel bandwidth well so that the transmitted video quality is degraded seriously. In this work, we propose a new H.263+ rate control scheme which supports the variable bit rate (VBR) channel through the frame rate adjustment. In particular, a fast realization of encoding frame rate control based on motion information within a sliding window is developed to efficiently determine the tradeoff between spatial and temporal qualities. Experimental results are provided to demonstrate the superior performance of the proposed scheme.
Context-Aware Patch-Based Image Inpainting Using Markov Random Field Modeling

In this paper, we first introduce a general approach for context-aware patch-based image inpainting, where textural descriptors are used to guide and accelerate the search for well-matching (candidate) patches. A novel top-down splitting procedure divides the image into variable size blocks according to their context, constraining thereby the search for candidate patches to nonlocal image regions with matching context. This approach can be employed to improve the speed and performance of virtually any (patch-based) inpainting method. We apply this approach to the so-called global image inpainting with the Markov random field (MRF) prior, where MRF encodes a priori knowledge about consistency of neighboring image patches. We solve the resulting optimization problem with an efficient low-complexity inference method. Experimental results demonstrate the potential of the proposed approach in inpainting applications like scratch, text, and object removal. Improvement and significant acceleration of a related global MRF-based inpainting method is also evident.
Robust joint sparsity model for hyperspectral image classification

Sparsity-based classification methods have been widely used in hyperspectral image (HSI) classification. These methods typically assumed Gaussian noise, neglecting the fact that HSIs are often corrupted by different types of noise in practice. In this paper, we develop a robust super-pixel level joint sparse representation classification model (RSJSRC) to address the mixed noise problem in sparsity-based HSI classification. Our method takes into account both Gaussian and sparse noise. Experimental results on simulated and real data demonstrate the efficiency of the proposed method and clear benefits from the introduced mixed-noise model.
Compressed Sensing using sparse binary measurements: A rateless coding perspective

Compressed Sensing (CS) methods using sparse binary measurement matrices and iterative message-passing recovery procedures have been recently investigated due to their low computational complexity and excellent performance. Drawing much of inspiration from sparse-graph codes such as Low-Density Parity-Check (LDPC) codes, these studies use analytical tools from modern coding theory to analyze CS solutions. In this paper, we consider and systematically analyze the CS setup inspired by a class of efficient, popular and flexible sparse-graph codes called rateless codes. The proposed rateless CS setup is asymptotically analyzed using tools such as Density Evolution and EXIT charts and fine-tuned using degree distribution optimization techniques.
Digital Image Processing of The Ghent Altarpiece: Supporting the painting's study and conservation treatment

Hanging in the Saint Bavo Cathedral in Ghent, Belgium, is The Ghent Altarpiece, also known as The Adoration of the Mystic Lamb . According to an inscription on the outer frames, it was painted by brothers Hubert and Jan van Eyck for Joos Vijd and?his wife Elisabeth Borluut in 1432. It is one of the most admired and influential paintings in the history of art and has given rise to many intriguing questions that have been puzzling art historians to date [11]. Moreover, the material history of the panels is very complicated. They were hidden, dismantled, moved away, stolen, and recovered during riots, fires and wars. The recovery of the panels by the U.S. Army in the Nazi hoards deep in the Altaussee salt mines has particularly marked memories. One panel was stolen in 1934 and never recovered. Besides varying conservation conditions, the panels underwent numerous restoration treatments and were even partially painted over.
Double reweighted sparse regression for hyperspectral unmixing

Spectral unmixing is an important technology in hyperspectral image applications. Recently, sparse regression is widely used in hyperspectral unmixing. This paper proposes a double reweighted sparse regression method for hyperspectral unmixing. The proposed method enhances the sparsity of abundance fraction in both spectral and spatial domains through double weights, in which one is used to enhance the sparsity of endmembers in the spectral library, and the other to improve the sparseness of abundance fraction of every material. Experimental results on both synthetic and real hyperspectral data sets demonstrate effectiveness of the proposed method both visually and quantitatively.
Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior

Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.
Filtering speckle noise in SAS images to improve detection and identification of seafloor targets

Multiplicative speckle noise is always present in synthetic aperture sonar (SAS) images, which is due to the coherent nature of scattering phenomena. Many methods that reduce speckle noise while preserving texture and detail have been developed for SAR and presented in the literature. In this paper, some speckle reduction techniques from SAR images are adapted and applied to SAS high resolution images. A comparison of different methods is performed and new improvements for traditional methods are introduced, considering image quality and better Speckle Suppression Index (SSI). These methods include mean adaptive filter, anisotropic diffusion, Lee filter, Kuan filter and a wavelet-based methodology, which are applied to real SAS high resolution (HR) data collected by NATO Underwater Research Center (NURC) during the COLOSSUS measurement campaign.
Exploiting the low-rank property of hyperspectral imagery: A technical overview

Hyperspectral images (HSIs) often suffer from various annoying degradations, which poses huge challenges for the practical applications. Fortunately, clean HSI is intrinsically low-rank, which opens up a broad category of HSI processing and analysis methods with high robustness against the complicated mixture of various noises and outliers. Based on the low rank property of HSI, this paper provides a comprehensive review on restoration, multiangle registration and unmixing methods for HSIs developed very recently, and insights for further investigations.
Hyperspectral unmixing by reweighted low rank and total variation

In recent years, sparse regression has drawn much attention in hyperspectral unmixing. The well known sparse unmixing via variable splitting augmented Lagrangian (SUnSAL) and sparse unmixing via variable splitting augmented Lagrangian and total variation (SUnSAl-TV) aim to find the sparsest abundance of every data vector individually. However, these methods ignore the global structure of all the vectors. In this paper, we propose a novel hyperspectral unmixing method by exploiting low rank property of the abundance matrix. Our proposed method find the lowest-rank representation of a collection of the abundance vectors by using reweighted low rank constraint. This way, our proposed unmixing method better captures the global structure of the abundance matrix and improve the accuracy of abundance estimation. Our approach also takes the spatial context into account by a TV constraint. Experimental results on both the synthetic and real hyperspectral data demonstrate the effectiveness of our proposed algorithm.
Three-dimensional quantitative microwave imaging of realistic numerical breast phantoms using Huber regularization

Breast tumor detection with microwaves is based on the difference in dielectric properties between normal and malignant tissues. The complex permittivity reconstruction of inhomogeneous dielectric biological tissues from microwave scattering is a nonlinear, ill-posed inverse problem. We proposed to use the Huber regularization in our previous work where some preliminary results for piecewise constant objects were shown. In this paper, we employ the Huber function as regularization in the even more challenging 3D piecewise continuous case of a realistic numerical breast phantom. The resulting reconstructions of complex permittivity profiles indicate potential for biomedical imaging.