nils-herrmann/clean_scirepeval huggingface dataset:
A Collaborative Scheduling-Based Parallel Solution for HEVC Encoding on Multicore Platforms

In order to meet the high computational demand to achieve superior coding efficiency and to explore the parallelism of parallel processing architectures, the emerging high efficiency video coding (HEVC) standard has been designed to be more parallelizable than previous video coding standards. However, it is still desirable to design an efficient parallel HEVC encoder to fully exploit the parallelism of the increasingly powerful multicore platforms, especially when considering the amount of parallelism, the scalability of parallelization, and the coding efficiency. In this work, a performance model of HEVC encoding is first introduced to investigate the speedup and the limitations of the technique of wavefront parallel processing (WPP) under various conditions. Then, a collaborative scheduling-based parallel solution (CSPS) for HEVC encoding is proposed, which includes adaptive parallel mode decision, asynchronous frame-level pixel interpolation, and multigrained task scheduling. The goal of the proposed CSPS is to defeat the disadvantages of WPP and further improve the parallelization of HEVC encoding on multicore platforms. Extensive experimental results demonstrate the efficiency of the proposed CSPS for parallelizing HEVC encoding as the computing resources of multicore architectures can be fully utilized.
Shape from Shading with a Linear Triangular Element Surface Model

The authors propose to combine a triangular element surface model with a linearized reflectance map to formulate the shape-from-shading problem. The main idea is to approximate a smooth surface by the union of triangular surface patches called triangular elements and express the approximating surface as a linear combination of a set of nodal basis functions. Since the surface normal of a triangular element is uniquely determined by the heights of its three vertices (or nodes), image brightness can be directly related to nodal heights using the linearized reflectance map. The surface height can then be determined by minimizing a quadratic cost functional corresponding to the squares of brightness errors and solved effectively with the multigrid computational technique. The proposed method does not require any integrability constraint or artificial assumptions on boundary conditions. Simulation results for synthetic and real images are presented to illustrate the performance and efficiency of the method.
Fast near-optimal energy allocation for multimedia loading on multicarrier systems

An efficient near-optimal energy allocation method for multimedia data transmitted using multicarrier modulation is investigated in this research. Our algorithm allocates energy to carriers by solving a simple set of linear equations, which is near-optimal for almost the entire SNR range. Furthermore, based on the proposed energy allocation algorithm, we develop asymptotic limits for the approximation error, the carrier energy, and the carrier rate. While limits for the special case with uniform noise and BPSK modulation were obtained before, our derivation applies to the more general case with nonuniform noise and higher-order M-QAM constellations. Finally, we link these limits with several types of multimedia and non-multimedia sources.
Tree model simplification with hybrid polygon/billboard approach and human-centered quality evaluation

We propose a tree model simplification method based on a hybrid polygon/billboard approach in this work. The objective is to create a simplified tree model with lower polygon counts while preserving the visual appearance of the original model. The result is suitable for human viewing from different angles around the tree as well as close up views. In the simplification process, a subset of polygons is carefully chosen to speed up the rendering process, and textures with a different number of leaves are mapped to scaled polygons based on the density of polygon's position on the tree. For objective performance evaluation, saliency maps of rendered images of simplified and original models are generated and the mean-squared-error (MSE) between them is calculated to quantify their resemblance. Furthermore, it is shown that feature maps (e.g., the intensity, orientation, and color maps of an image) can be utilized to simplify the tree model more aggressively since they provide means to highlight human visual attention. Finally, a subjective test is conducted to rate different simplification algorithms. Experimental results indicate that the proposed hybrid polygon/billboard method has better performance than other benchmarking methods in both objective and subjective evaluations.
A new approach to image retrieval with hierarchical color clustering

After performing a thorough comparison of different quantization schemes in the RGB, HSV, YUV, and CIEL*u*v* color spaces, we propose to use color features obtained by hierarchical color clustering based on a pruned octree data structure to achieve efficient and robust image retrieval. With the proposed method, multiple color features, including the dominant color, the number of distinctive colors, and the color histogram, can be naturally integrated into one framework. A selective filtering strategy is also described to speed up the retrieval process. Retrieval examples are given to illustrate the performance of the proposed approach.
Fast overlapped block motion compensation with checkerboard block partitioning

The overlapped block motion compensation (OBMC) scheme provides an effective way to reduce blocking artifacts in video coding. However, in comparison with the traditional block motion compensation (BMC), its complexity of motion estimation increases significantly due to the interdependency of motion vectors. In this work, a new fast motion estimation algorithm is proposed for OBMC based on the checkerboard block partitioning and grouping in encoding. This algorithm, called the grouped OBMC (GOBMC), effectively decorrelates dependency among OBMC motion vectors and facilitates the search process. In comparison with traditional iterative OBMC motion search, GOBMC performs motion estimation only once per macroblock at the encoder, and reaches a local optimal solution with degradation of 0.05-0.1 dB. At the decoding end, we also propose a fast OBMC reconstruction scheme which reduces the complexity of multiplication to 38% of that of traditional OBMC reconstruction while preserving the same visual quality as obtained via BMC search with OBMC reconstruction.
Receiver design for bit-interleaved MIMO-OFDM systems over time-varying channels

A complete solution to joint channel tracking and symbol detection for bit-interleaved MIMO-OFDM systems in a time-varying frequency-selective fading environment is proposed in this research. In the training mode, the Rao-Blackwellised particle filter (RBPF) is used to estimate the phase offset (PO) and the carrier frequency offset (CFO). In the data mode, based on a recursive EM procedure in conjunction with soft decoding, we develop an iterative algorithm that performs the minimum mean squared error (MMSE) channel estimation and the maximum a posterior (MAP) probability symbol detection jointly. The soft decoding module is composed of the list sphere decoder (LSD) and the soft-in-soft-out (SISO) channel decoder based on the BCJR algorithm. The performance of the proposed algorithm is evaluated via simulation. It is demonstrated that the new algorithm has robust performance in the presence of phase noise and the carrier frequency offset.
Fast mesh simplification for progressive transmission

A very fast mesh simplification algorithm based on half-edge collapse is proposed in this work with a focus on progressive graphic transmission applications. We generalize the Quadric Error Metric (QEM) method to accommodate new topological operations. A flatness criterion is combined with the defined metric to preserve the visual quality of simplified meshes. We further adopt the vertex-based priority queue to accelerate the simplification speed. It is shown by experimental results that our method can achieve similar results as the QEM algorithm with a faster speed.
Maximum-likelihood synchronization and channel estimation for OFDMA uplink transmissions

Maximum-likelihood estimation of the carrier frequency offset (CFO), timing error, and channel response of each active user in the uplink of an orthogonal frequency-division multiple-access system is investigated in this study, assuming that a training sequence is available. The exact solution to this problem turns out to be too complex for practical purposes as it involves a search over a multidimensional domain. However, making use of the alternating projection method, we replace the above search with a sequence of mono-dimensional searches. This results in an estimation algorithm of a reasonable complexity which is suitable for practical applications. As compared with other existing semi-blind methods, the proposed algorithm requires increased overhead but has more flexibility as it can be used with any subcarrier assignment scheme. Simulations indicate that the accuracy of the CFO estimates asymptotically achieves the Cramer-Rao bound.
The CNN as a Guided Multilayer RECOS Transform [Lecture Notes]

There is a resurging interest in developing a neural-network-based solution to the supervised machine-learning problem. The convolutional neural network (CNN) will be studied in this lecture note. We introduce a rectified-correlations on a sphere (RECOS)  transform as a basic building block of CNNs. It consists of two main concepts: 1) data clustering on a sphere and 2) rectification. We then interpret a CNN as a network that implements the guided multilayer RECOS transform with two highlights. First, we compare the traditional single-layer and modern multilayer signal-analysis approaches, point out key areas that enable the multilayer approach, and provide a full explanation to the operating principle of CNNs. Second, we discuss how guidance is provided by labels through backpropagation (BP) in the training.
A Statistical Approach to Contour Line Estimation in Wireless Sensor Networks With Practical Considerations

Contour line estimation in a monitored physical phenomenon using wireless sensor networks (WSNs) with cross-layer considerations is investigated in this paper. Herein, we present a statistical approach to locate contour points for contour line construction in the presence of irregular sensor deployment with the assistant of Delaunay triangular meshes. The proposed contour-estimation algorithm has certain error-correction capability, and it is robust to the data degradation coming from sensing and/or communication noise. To ensure the success of the proposed algorithm, we further analyze three sources of signal distortion: sensing noise, data quantization error, and data communication noise. We find that choosing a proper data quantization level can balance the communication cost and the desired signal quality. Meanwhile, the impact of channel impairment can be mitigated by the appropriate data-fusion mechanism. In this paper, an adaptive data-fusion mechanism is proposed to avoid excessive packet retransmissions. The correctness of the proposed approach is verified through simulation, and the effects of different system parameters on the overall system performance are also given to show the robustness of the proposed algorithm. This paper also offers some guidelines on the design of the WSN system.
Blind Recursive Tracking of Carrier Frequency Offset (CFO) Vector in MC-CDMA Systems

A recursive algorithm for estimating and updating the effective carrier frequency offset (CFO) vector in a multicarrier code-division multiple-access (MC-CDMA) system is proposed in this work. The recursive relation is derived based on the expectation maximization (EM) algorithm with a quadratic constraint. This new approach enables the use of linear estimation theory to tackle the CFO estimation problem with or without training data, which leads to an analytic CFO estimate in closed form. Furthermore, the multiple access interference (MAI) is mitigated using the second order statistics of the interference-plus-noise vector, which is updated in a recursive manner under the EM formulation, too. When reaching a converged estimate, a fixed-norm quadratic constraint is imposed so that the final CFO estimate is robust to an imprecise covariance matrix estimate caused by insufficient data samples. It is demonstrated by computer simulation that the performance of an MC-CDMA system without the CFO information can be restored by the proposed scheme in the sense that its bit error probability (BEP) performance is close to that with perfect CFO knowledge
Latent fingerprint detection and segmentation with a directional total variation model

Latent fingerprint detection and segmentation play a critical role in image forensics for law enforcement. Being collected from crime scenes, a latent fingerprint is often mixed with other components such as structured noise or other fingerprints. Existing fingerprint recognition algorithms fail to work properly for latent fingerprint images, since they are mostly applicable under the assumption that the image is already properly segmented and there is no overlap between the target fingerprint and other components. In this work, we present a novel directional total variation (DTV) model to achieve effective latent fingerprint detection and segmentation. As compared with existing total variation models, the proposed DTV model differentiates itself by considering spatial-dependent texture orientations in the TV computation, which is particularly suitable for images with oriented textures. We demonstrate the superior performance of the proposed DTV technique using images from the NIST SD27 latent fingerprint database.
Dynamic collision resolution and traffic scheduling for DOCSIS systems with QoS support

The data over cable service interface specifications (DOCSIS) of the Multimedia Cable Network System (MCNS) organization intends to support IP traffic over HFC (hybrid fiber/coax) networks with significantly higher data rates than analog modems and integrated service digital network (ISDN) links. The high speed access enables the delivery of high quality audio, video and interactive services. To support quality-of-service (QoS) for such applications, it is important for HFC networks to provide effective medium access and traffic scheduling mechanisms. We consider an HFC network that has a shared upstream channel for transmission from stations assigned with different service priorities to the headend. We first present a multilevel priority collision resolution scheme with adaptive contention window adjustment. This collision resolution scheme separates and resolves collisions for different classes of critically delay-sensitive and best effort traffics, thus achieving the capability of preemptive priorities. To enhance the performance of our scheme, we adopt a novel methodology in which the headend dynamically selects the optimal backoff window size according to the estimate of the number of contending stations for each priority class. A data transmission traffic scheduling policy with multiple priority queues is also employed in the headend. This scheduling strategy is used to satisfy bandwidth requirements of higher priority traffics. A set of simulation scenarios is conducted using OPNET to demonstrate the performance efficiency of the proposed scheme.
Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals

Steganalysis of the quantization index modulation (QIM) steganography in a low-bit-rate encoded speech stream is conducted in this research. According to the speech generation theory and the phoneme distribution properties in language, we first point out that the correlation characteristics of split vector quantization (VQ) codewords of linear predictive coding filter coefficients are changed after the QIM steganography. Based on this observation, we construct a model called the Quantization codeword correlation network (QCCN) based on split VQ codeword from adjacent speech frames. Furthermore, the QCCN model is pruned to yield a stronger correlation network. After quantifying the correlation characteristics of vertices in the pruned correlation network, we obtain feature vectors that are sensitive to steganalysis. Finally, we build a high-performance detector using the support vector machine (SVM) classifier. It is shown by experimental results that the proposed QCCN steganalysis method can effectively detect the QIM steganography in encoded speech stream when it is applied to low-bit-rate speech codec such as G.723.1 and G.729.
Improved priority access, bandwidth allocation and traffic scheduling for DOCSIS cable networks

The Data Over Cable Service Interface Specifications (DOCSIS) is intended to support IP flows over HFC (hybrid fiber/coax) networks with significantly higher data rates than analog modems and Integrated Service Digital Network (ISDN) links for high quality audio, video and interactive services. To support quality-of-service (QoS) for such applications, it is important for HFC networks to provide effective media access and traffic scheduling mechanisms. In this paper, we first present a multilevel priority collision resolution scheme with adaptive contention window adjustment. The proposed collision resolution scheme separates and resolves collisions for different traffic priority classes (such as delay-sensitive and best effort streams), thus achieving the capability for preemptive priorities. Second, a novel MAC (media access control) scheduling mechanism and a new bandwidth allocation scheme are proposed to support multimedia traffic over DOCSIS-compliant cable networks. It is shown through simulation results that throughput and delay performance have been improved for the transmission of real-time VBR (variable bit rate) traffic as compared to current DOCSIS specifications.
Error analysis of DOA estimation for short code CDMA systems with a beamforming approach

A beamforming filter bank approach has recently been proposed to estimate the direction of arrival (DOA) in short code code-division multiple-access (CDMA) systems, exploiting both the signal formats at time domain and spatial signatures. The error in DOA estimation is caused by the inverse of a correlation matrix that is obtained with finite data support. To mitigate the DOA estimation error, we may increase data support and/or antenna numbers. The variance of the DOA estimation error is analysed and expressed as a function of the data support and antenna numbers in this work. Our theoretical analysis is supported by experimental data by computer simulation.
Design of MAI-free MC-CDMA systems over frequency-selective fading channels via codeword selection

A scheme to eliminate completely the multiaccess interference (MAI) of the multicarrier code division multiple access (MC-CDMA) system in a frequency-selective fading environment using codeword selection is proposed. We show that, if the CDMA codewords are chosen to be a proper subset of the Hadamard-Walsh codes, the MC-CDMA system can be MAI-free so that the implementational complexity of the transceiver can be greatly reduced. This MAI-free property is analyzed theoretically and demonstrated via simulation results.
Design and analysis of channel-phase-precoded ultra wideband (CPPUWB) systems

A new indoor data communication scheme called the channel phase preceded ultra wideband (CPPUWB) system is proposed in this work. The proposed CPPUWB system is efficient in computational power saving by encoding the transmit symbol with a channelized codeword. The channelized codeword is determined by the channel phase information that is estimated at the receiver and then fed back to the transmitter. A method to estimate the channel phase information using training symbols is presented. For a given number of training symbols, we derive a lower bound for the average output SNR, which can be used to evaluate the system performance. Finally, an MMSE receiver is proposed to suppress the residual intersymbol interference (ISI) for the high data rate scenario
Rate-Distortion Optimized Rate Control for Depth Map-Based 3-D Video Coding

In this paper, a novel rate control scheme with optimized bits allocation for the 3-D video coding is proposed. First, we investigate the R-D characteristics of the texture and depth map of the coded view, as well as the quality dependency between the virtual view and the coded view. Second, an optimal bit allocation scheme is developed to allocate target bits for both the texture and depth maps of different views. Meanwhile, a simplified model parameter estimation scheme is adopted to speed up the coding process. Finally, the experimental results on various 3-D video sequences demonstrate that the proposed algorithm achieves excellent R-D efficiency and bit rate accuracy compared to benchmark algorithms.
WiiStick: Enhancing motion recognition capability for Wii systems

Nintendo's Wii system uses motion acceleration sensors to provide an intuitive user interface with limited motion recognition capabilities. In this work, we investigate these limitations, and propose a new system called WiiStick to enhance the motion recognition performance. One main limitation of Wii is that the direction of movement cannot be recognized accurately if the motion involves both accelerating and rotating. To overcome this difficulty, we propose to add an elastic stick and an IR emitter to the Wiimote. Besides, we develop a motion recognition algorithm based on signal analysis and pattern recognition techniques. As a result, motion can be recognized more effectively. It is shown by experimental results that the motion recognition capability of the Wii system can be greatly improved by the proposed WiiStick system.
An HMM-based approach to humming transcription

Providing natural and efficient access to the fast growing multimedia information, accommodating a variety of user skills and preferences, is a critical aspect of content-based information mining. Query by humming provides a natural means for content-based retrieval from music databases. A statistical pattern recognition approach for recognizing hummed or sung melodies is reported in this paper. Being data-driven, the proposed system aims at providing a robust front-end especially for dealing with variability in user's productions. The segment of a note in the humming waveform is modeled by a hidden Markov model (HMM) while data features such as pitch measures are modeled by Gaussian mixture models (GMM). Preliminary real-time recognition experiments are carried out based on humming data obtained from eight users and an overall correct recognition rate of around 80% is demonstrated.
Steganography in JPEG2000 compressed images

Information hiding in JPEG2000 compressed images is investigated in this research. The challenges of covert communication in this state-of-the-art image codec are analyzed and a steganographic scheme is then proposed to reliably embed high-volume data into the JPEG2000 bit-stream. A special mode of JPEG2000 is employed, and its usage and functions are explained and justified. Experimental results are given to demonstrate the performance of the proposed algorithm.