nils-herrmann/clean_scirepeval huggingface dataset:
Iterative detection and frequency synchronization for OFDMA uplink transmissions

The problem of frequency synchronization, channel estimation, and data detection for all active users in the uplink of an OFDMA system is investigated in this work. Since the exact maximum likelihood (ML) solution to this problem turns out to be too complex for practical purposes, we derive an alternative scheme that operates in an iterative fashion. At each step, the superimposed signals arriving at the base station (BS) are separated by means of the space-alternating generalized expectation-maximization (SAGE) algorithm. Each separated signal is then passed to an expectation-conditional maximization (ECM)-based processor that updates frequency estimates and performs channel estimation and data detection for each user. The resulting architecture is reminiscent of the parallel interference cancellation (PIC) receiver, where interference is generated and removed from the received signal to improve the system performance. Simulations indicate that the proposed scheme outperforms other benchmark solutions at the price of increased computational complexity
Lossless video compression with residual image prediction and coding (RIPC)

A lossless video compression scheme is proposed based on residual image prediction and coding (RIPC) in this work. We first study different prediction schemes used in lossless coding, and observe that prediction residuals are not white but in form of granular noise. This study reveals a need to treat residual images with granular noise carefully. Then, we propose a new scheme, called RIPC, to encode the residual image effectively. Experimental results show that the proposed RIPC scheme outperforms the H.264 lossless intra prediction (H.264-LS) and JPEG-LS with a bit rate saving of 20% and 12%, respectively.
Performance evaluation of approximately MAI-free multiaccess OFDM transceiver

The performance of an approximately MAI-free OFDM transceiver, called the repetitively coded multicarrier CDMA (RCMC-CDMA), is evaluated and compared with MC-CDMA in this work. In particular, its advantages, on the MAI reduction capability and the low implementational complexity are presented. We first demonstrate that RCMC-CDMA has the performance comparable to MC-CDMA using a linear MUD (multiuser detection) MMSE (minimum mean squared errors) detector, while it outperforms MC-CDMA with a MUD decorrelating detector. Then, the complexity of RCMC-CDMA is shown to be lower than that of MUD-based MC-CDMA.
On Robustness of Ultra-Wideband (UWB) Precoding Against Timing Jitter

The channel phase precoding (CPP) technique was recently proposed for the ultra-wideband (UWB) communication system in  to save the feedback overhead and the computational complexity as compared with the time-reversal prefllter (TRP) technique [2]. Two ideal assumptions have been made in both systems; namely, the availability of accurate channel information and perfect synchronization of transmitted pulses. In this work, we examine the impact of timing jitter on the performance of TRP and CPP and show that the CPP-UWB system is more robust against the timing jitter than the TRP- UWB svstem.
Analysis of asynchronous long-code multicarrier CDMA systems with correlated fading

The effects of inter-carrier interference (ICI) and aperiodic random spreading sequences on the performance of asynchronous multicarrier code-division multiple-access (CDMA) systems with correlated fading between sub-carriers are investigated in this research. To conduct theoretical analysis for the maximal ratio combining (MRC) receiver, random parameters including asynchronous delays, correlated Rayleigh fading, and spreading sequences are averaged to find the unconditional covariance matrix of the interference-plus-noise vector. We demonstrate that the ICI in the system proposed by Kondo and Milstein (1996) can be mitigated by assigning a common random spreading sequence over all sub-carriers for each user, rather than using a set of distinct spreading sequences, where code sequences are assumed perfectly time-synchronized. Moreover, the analytic expression for the bit error probability (BEP) can be obtained with the Gaussian approximation. Simulation results are used to demonstrate the accuracy of our analysis. Various design tradeoffs including the number of sub-carriers, fading correlations, ICI and multipath effect are presented in simulation. We conclude that, when properly choosing a sufficiently large number of sub-carriers, the benefit of mitigating multipath interference and exploiting potential hidden frequency diversity shall prevail the impairment brought by increased ICI.
Cooperative Communications in Resource-Constrained Wireless Networks

Cooperative communications have been proposed to exploit the spatial diversity gains inherent in multiuser wireless systems without the need of multiple antennas at each node. This is achieved by having the users relay each others messages and thus forming multiple transmission paths to the destination. In resource constrained networks, such as wireless sensor networks, the advantages of cooperation can be further exploited by optimally allocating the energy and bandwidth resources among users based on the available channel state information (CSI) at each node. In the first part of this article, we provide a tutorial survey on various power allocation strategies for cooperative networks based on different cooperation strategies, optimizing criteria, and CSI assumptions. In the second part, we identify the similarities between cooperative networks and several sensor network applications that utilize collaboration among distributed sensors to achieve the system goal. These applications include decentralized detection/estimation and data gathering. The techniques developed in cooperative communications can be used to solve many sensor network problems
A progressive approach for perceptual audio coding

A progressive approach to perceptual audio coding is presented in this paper. In contrast with existing perceptual audio coding schemes where the digital audio signal is encoded at a fixed bit rate, the progressive approach achieves fully embedded audio coding whose bit rate can be controlled dynamically, making it useful in audio networking applications. In the proposed scheme, efficient successive quantization and bit layer coding are applied for the compression of an audio signal representation obtained from traditional subband decomposition. An improved successive approximation quantization (ISAQ) is introduced to perform bit allocation and quantization based on the psychoacoustic analysis of input audio signals.
Objective Video Quality Assessment Based on Perceptually Weighted Mean Squared Error

Object quality assessment for compressed video is critical to various video compression systems that are essential in the video delivery and storage. Although mean squared error (MSE) is computationally simple, it may not be accurate to reflect the perceptual quality of compressed videos, which are also affected dramatically by the characteristics of the human visual system (HVS), such as contrast sensitivity, visual attention, and masking effect. In this paper, a video quality metric is proposed based on perceptually weighted MSE. A low-pass filter is designed to model the contrast sensitivity of the HVS with the consideration of visual attention. The imperceptible distortion is adaptively removed in the salient and nonsalient regions. To quantitatively measure the masking effect, the randomness of video content is proposed in both the spatial and temporal domains. Since the masking effect highly depends on the regularity of structure and motion in the spatial and temporal directions, the video signal is modeled as a linear dynamic system, and the prediction error of future frames from previous frames is used as randomness to measure the significance of masking. The relation is investigated between MSE and perceptual quality scores across various contents, and a masking modulation model is proposed to compensate the impact of the masking effect on the MSE. The performance of the proposed quality metric is validated on three video databases with various compression distortions. The experimental results demonstrate that the proposed algorithm outperforms other benchmark quality metrics.
Region-adaptive texture-aware image resizing

In this paper, we analyze the effect of texture regularity on the performance of image resizing (or called image retargeting), and then propose an efficient texture-aware resizing algorithm. Being perceived as unimportant due to spatial homogeneity, textured patterns are largely deformed (or warped) in existing image resizing algorithms. However, arbitrary warping without considering the specific texture property tends to lead to noticeable visual artifacts. To address this issue, we exploit region features, including the scale and the shape information, to preserve both local and global structures. Guided by region and contour information, we propose a mesh-based resizing technique, which is formulated as a nonlinear least squares optimization problem and solved by an iterative Gauss- Newton method. Texture redundancy is effectively reduced through texture regularity analysis and real-time texture synthesis. The superior performance of the proposed image resizing technique is demonstrated by experimental results.
Subjective and Objective Video Quality Assessment of 3D Synthesized Views With Texture/Depth Compression Distortion

The quality assessment for synthesized video with texture/depth compression distortion is important for the design, optimization, and evaluation of the multi-view video plus depth (MVD)-based 3D video system. In this paper, the subjective and objective studies for synthesized view assessment are both conducted. First, a synthesized video quality database with texture/depth compression distortion is presented with subjective scores given by 56 subjects. The 140 videos are synthesized from ten MVD sequences with different texture/depth quantization combinations. Second, a full reference objective video quality assessment (VQA) method is proposed concerning about the annoying temporal flicker distortion and the change of spatio-temporal activity in the synthesized video. The proposed VQA algorithm has a good performance evaluated on the entire synthesized video quality database, and is particularly prominent on the subsets which have significant temporal flicker distortion induced by depth compression and view synthesis process.
Advanced Film Grain Noise Extraction and Synthesis for High-Definition Video Coding

A technique for film grain noise extraction, modeling and synthesis is studied and applied to high-definition video coding in this paper. Film grain noise enhances the natural appearance of pictures in high-definition video and should be preserved in coded video. However, the coding of video contents with film grain noise is expensive. In previous art, it was proposed to enhance the coding performance by extracting film grain noise from the input video at the encoder as a preprocessing step, and by resynthesizing and adding it back to the decoded video at the decoder as a postprocessing step. In a novel implementation of this approach, we first remove film grain noise from image/video with a variational denoising approach without distorting its original content. Then, we present a parametric model (consisting of a small set of parameters) to generate film grain noise that is close to the actual one in terms of a couple of observed statistical properties, such as the power spectral density and the crosschannel spectral correlation. Under this framework, the coding gain of denoised video is higher while the visual quality of the final reconstructed video is well preserved. Experimental results are provided to demonstrate the superior performance of the proposed approach.
Fast sub-pel motion vector prediction via block classification

A technique to predict the optimal sub-pel motion vector (MV) position based on integer-pel MVs is investigated in this work. Although it is possible to find the optimal MV position by fitting a local error surface using integer-pel MVs, the minimum of the error surface may fall outside of the region of interest. By analyzing the behavior of the error surface, we propose a classification scheme that divides blocks into regular and irregular types. For a regular block, we adopt the traditional error surface model and solve its minimum as the predicted sub-pel MV position. For an irregular block, we developed a novel prediction scheme to predict the optimal sub-pel MV position. Experimental results are given to show the low complexity and good RD performance of the proposed sub-pel MV prediction scheme.
Embedded coding of 3D graphic models

A progressive compression method which encodes a 3D graphic models into an embedded bit stream is investigated. The coder first encodes the coarsest resolution of the model, and then includes the information of finer details gradually. A rate-distortion model is used in the integration of different types of bit streams into a single embedded bit stream to optimize the overall coding performance. This embedding property can be applied to progressive transmission, multi-resolution editing and level-of-detail control. Numerical experiments are provided to demonstrate the excellent rate-distortion performance of the proposed method.
Model-Based Frame-Layer Bit Allocation for Non-Conversational H.264 Video

The frame-layer bit allocation problem for non-conversational H.264 video rate control is studied in this work. For non-conversational applications, we first impose a GOP (group of pictures) structure on the underlying H.264 video. Then, under the assumption of frame's independence, a one-pass algorithm is developed using the Lagrange optimization framework and GOP-based rate modeling. Instead of estimating the R-D data of future frames to determine the Lagrange multiplier, we estimate the Lagrange multiplier directly from GOP rate models that specify the number of bits consumed in a GOP. It is shown by experimental results that the proposed bit allocation algorithm achieves higher picture quality than that used in the H.264 JM8.1a reference code.
Audiovisual-based adaptive speaker identification

An adaptive speaker identification system is presented in this paper, which aims to recognize speakers in feature films by exploiting both audio and visual cues. Specifically, the audio source is first analyzed to identify speakers using a likelihood-based approach. Meanwhile, the visual source is parsed to recognize talking faces using face detection/recognition and mouth tracking techniques. These two information sources are then integrated under a probabilistic framework for improved system performance. Moreover, to account for speakers' voice variations along time, we update their acoustic models on the fly by adapting to their newly contributed speech data. An average of 80% identification accuracy has been achieved on two test movies. This shows a promising future of the proposed audiovisual-based adaptive speaker identification approach.
A wavelet transform approach to texture analysis

Traditional texture analysis algorithms focus too much on the local coupling between image pixels. Time/frequency analytical tools such as the Gabor and wavelet transforms can efficiently characterize the coupling of different scales in textures, and help to overcome this difficulty. However, the conventional wavelet transform, which has a finer resolution in the lower frequency channels, does not work properly for textured images, since textures are quasi-periodic signals whose dominant frequencies are located in the middle frequency regions. In the present research, a tree-structured wavelet transform is proposed. An adaptive procedure is developed to zoom into any frequency channels with significant information so that the decomposition can be further performed. The application of the new transform to texture classification is demonstrated.<<ETX>>
DCT based error concealment for RTSP video over a modem Internet connection

Data lost in the form of dropped packets is a major obstacle to actualizing real time Internet video via modem connection with frame rates as high as 30 frames per second. Error concealment (EC) techniques must restore the lost data. Due to rigid system specifications of this application, only the computationally simplest error concealment methods are plausible. Replication, which is the simplest temporal error concealment (TEC) method, fits well in this situation. There are however circumstances when replication is inadequate and linear interpolation, the simplest spatial EC, is used in its place. In this work, we consider an alternative spatial EC to linear interpolation, i.e. the DCT-coefficient based EC algorithm, with comparable computational cost and consistently better image quality in real-time Internet video applications.
Blind recursive tracking of carrier frequency offsets in MC-CDMA systems

A blind recursive algorithm to effectively update the carrier frequency offset (CFO) vector in a multicarrier code-division multiple-access (MC-CDMA) system is studied in this research. The recursive relation is developed based on the expectation maximization (EM) algorithm with a quadratic constraint. This new perspective enables the use of linear estimation theory to deal with the blind CFO estimation problem, leading to an analytic CFO estimate in closed-form. The multiple access interference (MAI) is mitigated using the second order statistics of the interference-plus-noise vector, which can also be updated in a recursive manner under the EM formulation. Finally, we verify the effectiveness of the proposed algorithm with computer simulation.
Multi-hypothesis motion compensated prediction (MHMCP) for error-resilient visual communication

A multi-hypothesis motion compensated prediction (MHMCP) algorithm, which predicts a block from a weighted superposition of more than one reference block in the frame buffer, is proposed and analyzed for error resilient visual communication. By combining these reference blocks effectively, MHMCP can enhance the error resilient capability of compressed video as well as achieve a coding gain. In particular, we investigate the error propagation effect in the MHMCP coder and analyze the rate-distortion performance in terms of the hypothesis number and hypothesis coefficients. Simulation results are given to confirm the analysis.
A statistical multidimensional humming transcription using phone level hidden Markov models for query by humming systems

A new phone level hidden Markov model approach applied to human humming transcription is proposed in this research. A music note has two important attributes, i.e. pitch and duration. The proposed system generates multidimensional humming transcriptions, which contain both pitch and duration information. Query by humming provides a natural means for content-based retrieval from music databases, and this research provides a robust front-end for such an application. The segment of a note in the humming waveform is modeled by phone level hidden Markov models (HMM). The duration of the note segment is then labeled by a duration model. The pitch of the note is modeled by a pitch model using a Gaussian mixture model. Preliminary real-time recognition experiments are carried out with models trained by data obtained from eight human objects, and an overall correct recognition rate of around 84% is demonstrated.
Age Estimation via Grouping and Decision Fusion

We present a novel multistage learning system, called grouping estimation fusion (GEF), for human age estimation via facial images. The GEF consists of three stages: (1) age grouping; (2) age estimation within age groups; and (3) decision fusion for final age estimation. In the first stage, faces are classified into different groups, where each group has a different age range. In the second stage, three methods are adopted to extract global features from the whole face and local features from facial components (e.g., eyes, nose, and mouth). Each global or local feature is individually utilized for age estimation in each group. Thus, several decisions (i.e., estimation results) are derived. In the third stage, we obtain the final estimated age by fusing the diverse decisions from the second stage. To create diverse decisions for fusion, we investigate multiple age grouping systems in the first stage, where each system has a different number of groups and different age ranges. Thus, various decisions can be made from the second stage, and will be delivered to the third stage for fusion. Totally, six fusion schemes (i.e., intra-system fusion, inter-system fusion, intra-inter fusion, inter-intra fusion, maximum-diversity fusion, and composite fusion) are developed and compared. The performance of the GEF system is evaluated on the Face and Gesture Recognition Research Network and the MORPH-II databases, and it outperforms the existing state-of-the-art age estimation methods by a significant margin. That is, the mean absolute errors of age estimation are reduced from 4.48 to 2.81 years on FG-NET and 3.82 to 2.97 years on MORPH-II.
Exploring confusing scene classes for the places dataset: Insights and solutions

Scene classification is more challenging than object classification due to higher ambiguity in scene labels. In this work, we propose to use the filter weights at the last stage of a CNN model trained by the Places dataset, which is also known as the "scene anchor vector (SAV)", to explain the source of confusions. An SAV points to a cluster of images. If two anchor vectors have a smaller angle, we see overlapping image clusters, leading to a set of confusing classes. To overcome it, we propose to merge images associated with confusing anchor vectors into a confusion set and split the set in an unsupervised fashion to create multiple subsets. It is called the "automatic subset clustering (ASC)" process. Each of these subsets contains scene images of strong visual similarity. After the ASC process, we train a random forest (RF) classifier for each confusion subset to allow better scene classification. The ASC/RF scheme can be added on top of any existing scene-classification CNN as a post-processing module with little extra training effort. It is shown by extensive experimental results that, for a given baseline CNN, the ASC/RF scheme can offer a significant performance gain.
Internet Traffic Classification for Scalable QOS Provision

A new scheme that classifies the Internet traffic according to their application types for scalable QoS provision is proposed in this work. The traditional port-based classification method does not yield satisfactory performance, since the same port can be shared by multiple applications. Furthermore, asymmetric routing and errors of modern measurement tools such as PCF and NetFlow degrades the classification performance. To address these issues, the proposed classification process consists of two steps: feature selection and classification. Candidate features that can be obtained easily by ISP are examined. Then, we perform feature reduction so as to balance the performance and complexity. As to classification, the REPTree and the bagging schemes are adopted and compared. It is demonstrated by simulations with real data that the proposed classification scheme outperforms existing techniques