nils-herrmann/clean_scirepeval huggingface dataset:
A Cognitive MAC Protocol Using Statistical Channel Allocation for Wireless Ad-Hoc Networks

The MAC protocol of a cognitive radio (CR) device should allow it to access unused or under-utilized spectrum without (or with minimal) interference to primary users dynamically. To fulfill such a goal, we propose a cognitive MAC protocol using statistical channel allocation and call it SCA-MAC in this work. SCA-MAC is a CSMA/CA-based protocol, which exploits statistics of spectrum usage for decision making on channel access. For each transmission, the sender negotiates with the receiver on transmission parameters through the control channel. A model is developed for CR devices to evaluate the successful rate of transmission. A CR device should pass the threshold of the successful transmission rate via negotiation before it can begin a valid transmission on data channels. The operating range and channel aggregation are two control parameters introduced to maintain the MAC performance. To validate our ideas, we conducted theoretical analysis and simulations to show that SCA-MAC does improve the throughput performance and guarantee the interference to incumbents to be bounded by a predetermined acceptable rate. The proposed MAC protocol does not need a centralized controller, as the negotiation between the sender and the receiver is performed using the CSMA/CA-based algorithm.
Adaptive nonlocal means algorithm for image denoising

An adaptive image denoising technique based on the nonlocal means (NL-means) algorithm is investigated in this research. The proposed method first employs the singular value decomposition (SVD) method and the K-means clustering (K-means) technique for robust block classification in noisy images. Then, the local window is adaptively adjusted to match the local property of a block. Finally, a rotated block matching algorithm is adopted for better similarity matching. Experiment results are given to demonstrate the superior denoising performance of the proposed adaptive NL-means (ANL-means) denoising technique.
Robust on-line beat tracking with kalman filtering and probabilistic data association (KF-PDA)

A Kalman filtering (KF) approach to on-line musical beat tracking with probabilistic data association (PDA) is investigated in this work. We first formulate the beat tracking process as a linear dynamic system of beat progression, and then apply the Kalman filtering algorithm to the dynamic system in estimating the time-varying tempo and beat locations. Musical beat tracking using traditional Kalman filtering is however not reliable in the presence of tempo fluctuations and expressive timing deviations. To address this problem, we adopt data association techniques to assign probability masses to all possible beat interpretations, and then locate the true beat according to the weighting. Two methods are proposed. The first one (PDA-I) weighs the distance between the candidate observation and the predicted beat location while the second method (PDA-II) considers not only the distance but also the onset intensity in weight selection. Superior performance of the proposed beat tracking algorithm is demonstrated with simulation results on the Music Information Retrieval Evaluation Exchange (MIREX) 2006 beat tracking competition practice dataset and the Billboard Top-10 database.
Synchronization Techniques for Orthogonal Frequency Division Multiple Access (OFDMA): A Tutorial Review

Orthogonal frequency division multiple access (OFDMA) has recently attracted vast research attention from both academia and industry and has become part of new emerging standards for broadband wireless access. Even though the OFDMA concept is simple in its basic principle, the design of a practical OFDMA system is far from being a trivial task. Synchronization represents one of the most challenging issues and plays a major role in the physical layer design. The goal of this paper is to provide a comprehensive survey of the latest results in the field of synchronization for OFDMA systems, with tutorial objectives foremost. After quantifying the effects of synchronization errors on the system performance, we review some common methods to achieve timing and frequency alignment in a downlink transmission. We then consider the uplink case, where synchronization is made particularly difficult by the fact that each user's signal is characterized by different timing and frequency errors, and the base station has thus to estimate a relatively large number of unknown parameters. A second difficulty is related to how the estimated parameters must be employed to correct the uplink timing and frequency errors. The paper concludes with a comparison of the reviewed synchronization schemes in an OFDMA scenario inspired by the IEEE 802.16 standard for wireless metropolitan area networks.
Object-oriented harmonization of multimedia XML applications

The problem of creating powerful XML applications by reutilizing and integrating functionalities from existing XML applications, called XML harmonization, is studied in this research. An object-oriented (OO) data model is first proposed. Then, the corresponding harmonization methodology for XML applications is presented. That is, we develop the harmonized extensible markup language (HXML) according to the proposed OO data model. HXML provides a uniform interface for users to access functionalities and objects of various XML applications being harmonized. An example that harmonizes two multimedia XML applications, i.e. synchronized multimedia integration language (SMIL) and extensible 3D (X3D), using the OO technique has been implemented and compared with the extensible MPEG-4 textual format (XMT). Our methodology provides better extendibility than any previous work in this emerging research field.
Iterative Equalization and Decoding for Unsynchronized OFDMA Uplink Transmissions

For the uplink transmission of a coded OFDMA system, we present an iterative receiver that performs joint frequency offset acquisition, channel estimation and maximum a posteriori (MAP)-based decoding for each active user in this work. The proposed receiver attempts to separate users' combined signals by resorting to the space-alternating generalized expectation-maximization (EM) algorithm. Each separated user's signal is then passed to an expectation-conditional maximization (ECM)-based processor that jointly performs frequency acquisition, channel estimation and decoding at each iteration. As compared with conventional OFDMA systems that employ hard- decision equalization and decoding, the proposed receiver can exploit the soft-decision feedback derived from the MAP decoder to provide more reliable synchronization, channel estimation and interference suppression. Simulations indicate that the proposed scheme provides accurate decoding for unsynchronized OFDMA uplink transmissions over frequency-selective fading channels.
Network-aware error control using smooth and fast rate adaptation mechanism for TCP-friendly Internet video

Interactive error control to mitigate error propagation from packet loss is one of the serious challenges to Internet video applications to date. Error control scheme performance is highly dependent on how to coordinate the source error resilience options based upon the observed network status, especially for real-time interactive (i.e. unicast) Internet video. In this paper, a smooth and fast rate adaptation mechanism (SFRAM) (Kim et al.,  2000, May 2000), which does TCP-friendly rate adaptation based on the TCP throughput equation, is used to coordinate the network-aware error control (NAEC) scheme for the best end-to-end video quality. Since the adopted smoothing and weighting scheme of SFRAM enables the capture of only distinct network variations, the various source-level error resilience tools such as reference picture selection (RPS), video redundancy coding (VRC) and I-MB (intra-macroblock) refresh can be dynamically switched. To figure out the best operating mode of interactive error control upon the network status (e.g., round trip time and packet loss rate), performance of each error resilience option is first investigated with a virtual packet loss and feedback scenario. Then, extensive ns-2 network simulation with integrated ITU-T H.263+ encoder is conducted to confirm the efficiency of the proposed NAEC with realistic TCP background traffic and feedback while it behaves in a TCP-friendly way. From simulation, SFRAM-driven NAEC demonstrates enhanced error control over fixed error schemes.
The generalized Backus-Gilbert inversion method for signal recovery in multiresolution spaces

The Backus-Gilbert (BG) method provides a signal recovery algorithm based on the moment information of a signal. In this work, we focus on a class of signals that can be well approximated with a multiresolution model, and propose a generalized BG method for signal recovery. A numerical experiment is given to demonstrate the performance of the generalized BG method.<<ETX>>
A GMM-based stair quality model for human perceived JPEG images

Based on the notion of just noticeable differences (JND), a stair quality function (SQF) was recently proposed to model human perception on JPEG images. Furthermore, a k-means clustering algorithm was adopted to aggregate JND data collected from multiple subjects to generate a single SQF. In this work, we propose a new method to derive the SQF using the Gaussian Mixture Model (GMM). The newly derived SQF can be interpreted as a way to characterize the mean viewer experience. Furthermore, it has a lower information criterion (BIC) value than the previous one, indicating that it offers a better model. A specific example is given to demonstrate the advantages of the new approach.
Robust and Secure Secret Sharing in Wireless Networks with Convolutional Network Coding

We investigate methods to enable robust and secure sharing among trusted peers in wireless erasure networks based on network coding (NC). The proposed communication model allows an eavesdropper to take advantage of the broadcast medium to successfully overhear messages transmitted along a min-cut. In situations where there is no secure capacity, we give a convolutional NC scheme that achieves weak secrecy to decrease the number of symmetric keys required. Furthermore, we demonstrate the fundamental duality between robustness and secrecy enforced by the wireless environment.
Iterative MAP channel estimation and multiuser detection for DS-CDMA in frequency-selective fading channels

A joint channel estimation and symbol detection scheme is proposed for asynchronous code-division multiple-access systems over multipath fading channels. A low-complexity Kalman-like filter is developed for blind channel tracking using the recursive expectation maximization (EM) algorithm and the maximum a posteriori probability (MAP) symbol detection method. The recursive EM algorithm yields channel estimates with discrete valued phase ambiguities. By exploiting the statistical properties of the EM-based blind estimators as well as the robustness of noncoherent MAP detection to phase errors, a method to resolve the phase ambiguity problem even in the presence of a deep fading environment is proposed. Based on the proposed blind channel estimator and phase corrector, an iterative structure is introduced for joint channel estimation, symbol detection and soft multiple access interference cancellation.
Robust joint channel estimation and symbol detection over MIMO channels using EM algorithm

An expectation maximization (EM) algorithm for joint channel tracking and symbol detection at the receiver in a multi-input multi-output (MIMO) frequency-selective fading environment is proposed in this research. Based on the recursive EM procedure in conjunction with the BCJR algorithm, 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 per- formance of the proposed algorithm is evaluated via simulation and compared with that of Kalmanfiltering with hard decision feedback. It is also demonstrated that the new algorithm has robust performance in the presence of a severe channel model mismatch.
Design and evaluation of suboptimal call admission control policy for OVSF code assignment in CDMA networks

A dynamic OVSF code assignment scheme employing a suboptimal call admission control (CAC) policy for CDMA networks is studied. Our focus is on the design of the suboptimal CAC policy such that the resulting code assignment can achieve an average data throughput close to that of the optimal policy while demanding a much lower implementational complexity. By observing the behavior of system dynamics over only two consecutive stages starting from an event of call arrival, we can significantly reduce the complexity associated with Markov decision process over the infinite time horizon from which the optimal policy is derived. The superior performance of the proposed suboptimal CAC policy is demonstrated via computer simulation.
Recovering missing coefficients in DCT-transformed images

A general method for recovering missing DCT coefficients in DCT-transformed images is presented in this work. We model the DCT coefficients recovery problem as an optimization problem and recover all missing DCT coefficients via linear programming. The visual quality of the recovered image gradually decreases as the number of missing DCT coefficients increases. For some images, the quality is surprisingly good even when more than 10 most significant DCT coefficients are missing. When only the DC coefficient is missing, the proposed algorithm outperforms existing methods according to experimental results conducted on 200 test images. The proposed recovery method can be used for cryptanalysis of DCT based selective encryption schemes and other applications.
WSN09-5: Anonymous Peer-to-peer Communication Protocol over Mobile Ad-hoc Networks

An efficient anonymous communication protocol, called MANET anonymous peer-to-peer communication protocol (MAPCP), for P2P applications over mobile ad-hoc networks (MANETs) is proposed in this work. MAPCP employs broadcasts with probabilistic flooding control to establish multiple anonymous paths between communication peers. It requires no hop-by-hop encryption/decryption along anonymous paths and, hence, demands lower complexity of computation and power consumption than other anonymous routing protocols for MANETs. Since MAPCP builds multiple paths to multiple peers within a single query phase without using an extra route discovery process, it is more efficient in P2P applications. Through analysis and extensive simulations, we demonstrate that MAPCP always maintains a higher degree of anonymity than a MANET anonymous single-path routing protocol in a hostile environment. Simulation results also show that MAPCP is resilient to passive attacks in data forwarding for both one-to-one and one-to-many communications.
Measuring and Predicting Tag Importance for Image Retrieval

Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems. However, all tags are treated as equally important in these systems, which may result in misalignment between visual and textual modalities during MIR training. This will further lead to degenerated retrieval performance at query time. To address this issue, we investigate the problem of tag importance prediction, where the goal is to automatically predict the tag importance and use it in image retrieval. To achieve this, we first propose a method to measure the relative importance of object and scene tags from image sentence descriptions. Using this as the ground truth, we present a tag importance prediction model to jointly exploit visual, semantic and context cues. The Structural Support Vector Machine (SSVM) formulation is adopted to ensure efficient training of the prediction model. Then, the Canonical Correlation Analysis (CCA) is employed to learn the relation between the image visual feature and tag importance to obtain robust retrieval performance. Experimental results on three real-world datasets show a significant performance improvement of the proposed MIR with Tag Importance Prediction (MIR/TIP) system over other MIR systems.
Rate control of H.263 for low bit rate visual communication

H.263 is an emerging video compression standard for low bit rate visual communication. A new rate control for H.263 is studied. The requirements for the rate control algorithm depend on the applications. For more flexible rate control, the frame rate is considered as a control variable. A frame rate control algorithm which can minimize the temporal quality degradation caused by a variable frame rate and estimate the supportable frame rate fast, is proposed. Also, an efficient global bit allocation algorithm of H.263, which can guarantee the reasonable sub-optimality and reduce the computational complexity, is proposed. Finally, experimental results are provided to demonstrate the better performance of the proposed scheme.
Channel diagonalization using a full-rate space-time block code

A novel full rate space-time block code that di- agonalizes the channel matrix without the channel knowl- edge at the transmitter is proposed in this research. The main advantage of channel diagonalization is that it allows a simple detection scheme at the receiver end. Our method for channel matrix diagonalization consists of two steps. First, we propose a code structure that can transform the channel matrix to a circulant one. Then, the circulant chan- nel matrix can be diagonalized using DFT and IDFT in the transmitter and the receiver, respectively. The proposed scheme not only diagonalizes any channel matrix with an even number of transmit antennas, but also guarantees that the real and imaginary parts of each received symbol are attenuated equally. Simulation results show that the pro- posed code structure outperforms the G4 code about 2 dB in an environment of four transmit and four receive antennas with the bit error probability equal to 10�6.
Shape reconstruction from photometric stereo

Two iterative algorithms for shape reconstruction based on multiple images taken under different lighting conditions, known as photometric stereo, are proposed. It is shown that single-image shape-from-shading (SFS) algorithms have an inherent problem, i.e., the accuracy of the reconstructed surface height is related to the slope of the reflectance map function defined on the gradient space. This observation motivates the authors to generalize the single-image SFS algorithm to two photometric stereo SFS algorithms aiming at more accurate surface reconstruction. The two algorithms directly determine the surface height by minimizing a quadratic cost functional, which is defined to be the square of the brightness error obtained from each individual image in a parallel or cascade manner. The optimal illumination condition that leads to best shape reconstruction is examined.<<ETX>>
Error correction for wireless image communication with a rate-distortion model

Rate-distortion, based error control schemes to reduce the error rate in wireless image communication are examined. Although error correction codes can effectively protect transmitted bits, the introduced overhead bits decreases the available channel capacity. Based on the rate-distortion model of embedded wavelet image coders, an unequal error protection scheme with error resilience is designed. That is, according to the different importance of bits, packets of bits in the compressed image data are protected by error correction codes with different correction capability. The experimental results in this work show the proposed rate-distortion based error correction scheme good error protection as well as low overhead in the compressed bitstream.
A dual graph approach to 3D triangular mesh compression

The triangular mesh provides one of the most popular representations for 3D graphic models. A typical triangular mesh consists of two different types of data: topological data which specify the connectivity of the mesh and geometrical data which describe information associated with each individual vertex or triangle. We propose a new compression scheme which encode topological data by using the dual graph of the original mesh. It is found that the dual graph can be represented as a degraded binary tree. Furthermore, geometrical data can be coded progressively with local prediction and embedded entropy coding. Experimental results show that an acceptable quality level can be reached at a compression ratio of 60 to 1 for general test models.
Ultrawideband Transceiver Design Using Channel Phase Precoding

A novel transceiver design for ultrawideband (UWB) communication systems using the channel phase precoding (CPP) technique is proposed in this work. With the CPP-UWB transceiver, we encode data symbols using the reversed order of the channel phase. A simple phase estimation algorithm is presented for the CPP-UWB implementation. Owing to its ability to coherently combine the channel magnitude of every multipath, the CPP-UWB transceiver can achieve a higher data rate by shortening its symbol duration with a tolerable interference. The performance of the CPP-UWB can be further improved using an optimal code length and/or the MMSE receiver to suppress intersymbol interference.
Robust traffic event extraction via content understanding for highway surveillance system

A method to extract traffic events by integrating the low-level, middle-level, and high-level feature extraction modules is developed in this research. The low-level module extracts features such as motion, size, and location. The middle-level module builds a bridge between the road surface plane in the real world and the captured image plane via geometric analysis. Finally, the high-level module identifies traffic events such as "traffic jam", "lane change", and "traffic rule violation", which require the understanding of video content in a specific knowledge domain. In the high-level module, various traffic events are related to motion characteristics obtained from the middle-level module. It is demonstrated by experimental results that the proposed system can achieve robust traffic event extraction.
Multi-Antenna Multicasting with Opportunistic Multicast Scheduling and Space-Time Transmission

Physical layer multicasting with opportunistic user selection and optimized space-time transmission is examined in this work. We consider a multi-antenna downlink scenario where a multi-antenna base-station transmits common information to a given set of users, each with a single receive antenna. In the past, most existing multi-antenna multicasting solutions have been restricted to spatial multiplexing and transmit beamforming. Here, we adopt the opportunistic multicast scheduling (OMS) scheme, where messages are encoded using fountain codes and an optimal subset of users is targeted for reception in each time slot. Capitalizing on extreme value theory, we derive analytical expressions for the average system throughput and utilize this result to obtain the optimal user selection ratio for both spatial multiplexing and transmit beamforming scenarios. To better utilize the channel state information available at the transmitter, we further consider an optimized space-time transmission (OST) scheme where the statistics of the space-time codeword is chosen to maximize the rate of the worst selected user. For reduced complexity, we utilize a semi-orthogonal user selection algorithm to determine the spatial dimensions of the signal and derive optimal power allocation across these dimensions. The proposed OST is a generalization of transmit beamforming and spatial multiplexing and, thus, outperforms both conventional schemes.