nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive Motion Search Range Prediction for Video Encoding

An adaptive motion search range (SR) prediction algorithm for video coding is proposed in this letter. To determine a proper SR size for motion vectors (MVs) is important to video encoding, since a good choice helps reduce the memory access bandwidth while maintaining the rate-distortion coding performance. To achieve this objective, we first obtain a motion vector predictor (MVP) for a target block based on MVs of its spatially and temporally neighboring blocks, which form a MV prediction set. Then, we relate the variance of the MV prediction set to the SR. That is, a larger variance implies lower accuracy of the MVP and, thus, a larger SR. Finally, we derive a probability model for the motion vector prediction difference, the difference between the optimal MV and the MVP, to quantify the probability for a chosen SR to contain the optimal MV. The superior performance of the proposed SR selection algorithm is demonstrated by experimental results.
Multimedia storage security in cloud computing: An overview

In this work, we conduct an in-depth survey on recent multimedia storage security research activities in association with cloud computing. After an overview of the cloud storage system and its security problem, we focus on four hot research topics. They are data integrity, data confidentiality, access control, and data manipulation in the encrypted domain. We describe several key ideas and solutions proposed in the current literature and point out possible extensions and futuristic research opportunities. Our research objective is to offer a state-of-the-art knowledge to new researchers who would like to enter this exciting new field.
A deep learning approach to drone monitoring

A drone monitoring system that integrates deep- learning-based detection and tracking modules is proposed in this work. The biggest challenge in adopting deep learning methods for drone detection is the limited amount of training drone images. To address this issue, we develop a model-based drone augmentation technique that automatically generates drone images with a bounding box label on drone's location. To track a small flying drone, we utilize the residual information between consecutive image frames. Finally, we present an integrated detection and tracking system that outperforms the performance of each individual module containing detection or tracking only. The experiments show that, even being trained on synthetic data, the proposed system performs well on real world drone images with complex background. The USC drone detection and tracking dataset with user labeled bounding boxes is available to the public.
Performance analysis of multicarrier CDMA systems with frequency offsets and random spreading under optimum combining

The carrier-frequency offset effect on the performance of asynchronous multicarrier direct-sequence code-division multiple-access (MC-DS-CDMA) systems with aperiodic random spreading and correlated Rayleigh fading is studied in this paper. We obtain the optimum combining filter that maximizes the signal-to-interference-plus-noise ratio (SINR) of the combined statistics and exploits correlated information among subchannels. A closed-form expression for the unconditional covariance matrix of the interference-plus-noise vector, which forms the basis of our theoretical analysis of the maximum SINR and the average bit error probability formula, is derived by averaging several random parameters including asynchronous delays, correlated Rayleigh fading, and signature sequences. The analytic results obtained are applicable to MC-CDMA with appropriate modifications. Furthermore, we show that the MC-CDMA system with a common random signature sequence over all subcarriers for a given user outperforms that with distinct sequences over different subcarriers. Finally, the performance of MC-CDMA systems using the optimum combining technique is compared with that of different combining filters in the simulation.
Understanding benford's law and its vulnerability in image forensics

In this paper, we attempt to shed light on Benford's law from the viewpoint of probability theory and point out its limitation in image forensic applications. First, we consider a generalized form of Benford's law and relate it to a random variable of a certain probability density function such as the generalized Gaussian or Laplacian function. Then, we examine the application of the generalized Benford's law to image forensic applications and point out such an application is vulnerable to the histogram manipulation attack.
Data hiding domain classification for blind image steganalysis

A statistical feature-based scheme is proposed to identify the data hiding domain of an embedded signal in this research. Two phenomena are observed for images before and after data hiding. First, the gradient energy increases as the continuity of gray levels between adjacent pixels is disturbed by the embedded signal. Second, the statistical variance of the coefficient distribution in macro-blocks tends to decrease after data hiding. These phenomena are analyzed mathematically. Then, statistical features in the pixel, DCT, and DWT domains are extracted and a maximum likelihood ratio test is adopted to solve the hiding domain classification problem. The proposed scheme has demonstrated good classification results
Perceptual image quality assessment using block-based multi-metric fusion (BMMF)

A new block-based multi-metric fusion (BMMF) approach is proposed for perceptual image quality assessment. The proposed BMMF scheme automatically detects image content and distortion types in a block via machine learning, which is motivated by the observation that the performance of an image quality metric is highly influenced by these factors. Locally, image block content is classified into three types; namely, smooth, edge and texture. Image distortion is detected and grouped into five types. An appropriate image quality metric is adopted for each block by considering its content and distortion types, and then all block-based quality metrics are fused to result in one final score. Furthermore, a corrected version of BMMF is derived for a specific group of distortions based on image complexity analysis. The proposed BMMF scheme is tested on TID database with its Spearman Correlation equal to 0.9471, which outperforms today's state-of-the-art image quality metrics.
A novel iterative receiver for uplink OFDMA

In this work we consider the uplink of an OFDMA system and present a novel receiver that performs joint frequency offset acquisition, channel estimation and data detection for each active user. The proposed receiver can be used with any subcarrier assignment scheme, and it operates in an iterative fashion. Users' separation is accomplished at the base station through space-alternating generalized-expectation (SAGE) techniques. Each separated user's signal is then passed to an expectation-conditional maximization (ECM)-based processor that jointly performs frequency acquisition, channel estimation and data detection at each iteration. Compared to conventional OFDMA systems, the proposed receiver allows significant reduction of the synchronization overhead as it dispenses from the need of returning estimated frequency offsets back to active users for frequency adjustment. Simulations indicate that the proposed scheme provides accurate data detection for unsynchronized OFDMA uplink transmissions over doubly-selective fading channels
Complexity Reduction of Maximum-Likelihood Multiuser Detection (ML-MUD) Receivers with Carrier Interferometry Codes in MC-CDMA

We propose to use carrier interferometry (CI) codes to reduce the complexity of the maximum- likelihood multiuser-detection (ML-MUD) receiver in MC-CDMA systems with carrier frequency offsets (CFOs). Complexity reduction is achieved by exploiting the sparsity of the cross-correlation matrix of CI codes. It is shown that, for a fully-loaded MC-CDMA with spread gain N over a multipath channel with length L, the complexity of the ML-MUD receiver grows exponentially with 2L-1 (instead of N), where L << N in a typical setting. Moreover, an upper bound for the minimum error probability is derived. Finally, simulation results are given to corroborate theoretical results.
Fast compression artifact reduction technique based on nonlinear filtering

A computationally efficient postprocessing technique to reduce compression artifacts in low-bit-rate video coding is presented in this research. Blocking and ringing effects are two major compression artifacts observed in the block DCT-based video codec, such as H.263, when the coding bit rate becomes low. Reduction of these artifacts can significantly improve the overall visual quality of decoded video. A new table-lookup method and a nonlinear filtering approach are adopted to remove the blocking and ringing effects, respectively. Experiments employing H.263+TMN8 anchor bit-streams are performed to show that the proposed postprocessing technique can alleviate the coding artifacts efficiently with a low computational complexity.
Fast image synthesis using an extended self-similar (ESS) model

Proposes a method called incremental Fourier synthesis to generate images based upon the 2-D extended self-similar (ESS) model. This algorithm creates the stationary increments of ESS processes by Fourier synthesis. Then, the increments are added up to generate the nonstationary 2-D ESS process. Because the new method can take advantage of the FFT, its computational complexity is only O(N/sup 2/ log/sub 2/(N)), and its memory requirement is O(N/sup 2/) for an image of size N/spl times/N.
Optimal Sink Deployment for Distributed Sensing of Spatially Nonstationary Phenomena

The optimal deployment of sinks in a sensor region for power efficient data gathering of a physical phenomenon is investigated in this work. In the system of consideration, nodes perform lossless distributed coding of the sensed data and the spatial statistics of the monitored phenomenon are possibly nonstationary due to heterogeneity in the sensing environment (e.g. variations in the altimetric profile). Non-stationary spatial statistics lead to uneven spatial profiles of the bit rates, unlike stationary statistics. The properties of rate profiles and the consequent optimal sink locations for a broad class of spatially non-stationary covariance models are studied by mathematical analysis and numerical examples.
Real-time compression artifact reduction via robust nonlinear filtering

Low complexity postprocessing algorithms to reduce compression artifacts by using a robust nonlinear filtering approach and a table lookup method are investigated in this research. We first formulate the compression artifact reduction problem as a robust estimation problem. Under this framework, an enhanced image can be obtained by minimizing a cost function that accounts for the image smoothness as well as the image fidelity constraints. However, unlike traditional methods that adopt a gradient descent method to search for the optimal solution, we determine the approximate solution via the evaluation of a set of nonlinear cost functions. This nonlinear filtering process is performed to reduce the computational complexity of the postprocessing operation so that it can be implemented in real time. In the case of video postprocessing, a table lookup method is adopted to further reduce the complexity. The proposed approach is generic and flexible. It can be applied to different compression schemes with minor finetuning. We have tested the developed algorithm on several compressed video or images which are obtained by JPEG 2000 VM, and H.263+. It has been demonstrated that the proposed method can reduce compression artifacts efficiently with a low computational complexity.
A Graph Approach to Dynamic Fractional Frequency Reuse (FFR) in Multi-Cell OFDMA Networks

A graph-based framework for dynamic fractional frequency reuse (FFR) in multi-cell OFDMA networks is proposed in this work. FFR is a promising resource allocation technique that can effectively mitigate inter-cell interference (ICI) in OFDMA networks. The proposed scheme enhances the conventional FFR by enabling adaptive spectral sharing per cell load conditions. Such adaptation has significant benefits in a practical environment where traffic load in different cells may be asymmetric and time-varying. The dynamic feature is accomplished via a graph approach in which the resource allocation problem is translated to a graph coloring problem. Specifically, in order to incorporate various versions of FFR in our framework, we construct a graph that matches the specific version of FFR and then color the graph using the corresponding graph algorithm. The performance improvement enabled by the proposed dynamic FFR scheme is further demonstrated by computer simulation for a 19-cell network with asymmetric cell load. For instance, the proposed dynamic FFR scheme can achieve a 12% and 33% gain in cell throughput and service rate over conventional FFR, and render a 70% and 107% gain in cell throughput and service rate with respect to the reuse-3 system.
Statistical Edge Detection with Distributed Sensors under the Neynan-Pearson (NP) Optimality

A statistical approach to distributed edge region detection in wireless sensor networks, which is optimized under the Neyman-Pearson (NP) criterion, is proposed in this work. The concept of edge nodes is adopted to label the defined edge region. Even though statistical methods have been proposed to detect edge nodes, a rigorous way to select the threshold value is lacking. Based on the NP criterion, a decision-fusion approach is developed to address the problem of threshold selection. Performance comparison of the proposed approach and the classifier-based approach is conducted. Simulation results show that the proposed approach is more stable and outperforms the classifier-based approach when there is a location error
Robust graphics streaming in walkthrough virtual environments via wireless channels

A graphics streaming system for walkthrough virtual environments is presented that facilitates reliable delivery and interactive browse of 3D models via wireless channels. Models are compressed by a partitioning-based progressive mesh compression algorithm to enable decorrelation of partitions and offer finer granularity. Then, the data of the virtual world are prioritized and organized according to viewer's requests. Finally, a transmission scheme is developed to adapt the sending rate to the available bandwidth and offer data protection techniques. Experimental results demonstrate that the proposed system reduces the required transmission bandwidth and provides an acceptable visual quality even at low bit rates.
Low-complexity video error concealment for mobile applications using OBMA

Low-complexity error concealment techniques for mobile applications are studied extensively in this paper. The boundary matching algorithm (BMA) is an attractive choice for mobile video concealment due to its low complexity. Here, we examine a variant of BMA called the outer boundary matching algorithm (OBMA). Although BMA and OBMA are similar in their design principle, it is empirically observed that OBMA outperforms BMA by a significant margin (typically, 0.5 dB or higher) while maintaining the same level of complexity. In this work, we attempt to explain the superior performance of OBMA, and conclude that OBMA provides an excellent tradeoff between the complexity and the quality of concealed video for a wide range of test video sequences and error conditions.
Performance Comparison of OFDM-TDMA and OFDMA with Cross-Layer Consideration

Performance comparison of multiuser OFDM-TDMA and OFDMA systems by considering physical and link cross-layer behavior is conducted in this work. We provide a new framework for performance evaluation that centers on "scheduling" with several different subcarrier/bit allocation schemes. The performance metrics include: the bit rate, the bit error rate (BER), packet throughput and delay. The instantaneous channel conditions of users are assumed to be available at the base station, which can be achieved by channel estimation at the mobile terminal (MT), and then fed back to the base station through a control channel. To perform the link layer comparison, we model the Rayleigh fading channel with a finite-state Markov chain, and adopt the M/G/1 queueing model as the analytical tool. Computer simulation is performed to verify conducted analytical results.
Cross-Examination for Angle-Closure Glaucoma Feature Detection

Effective feature selection plays a vital role in anterior segment imaging for determining the mechanism involved in angle-closure glaucoma (ACG) diagnosis. This research focuses on the use of redundant features for complex disease diagnosis such as ACG using anterior segment optical coherence tomography images. Both supervised [minimum redundancy maximum relevance (MRMR)] and unsupervised [Laplacian score (L-score)] feature selection algorithms have been cross-examined with different ACG mechanisms. An AdaBoost machine learning classifier is then used for classifying the five various classes of ACG mechanism such as iris roll, lens, pupil block, plateau iris, and no mechanism using both feature selection methods. The overall accuracy has shown that the usefulness of redundant features by L-score method in improved ACG diagnosis compared to minimum redundant features by MRMR method.
A repetitively coded multicarrier CDMA (RCMC-CDMA) transceiver for multiuser communications

This work proposes an approximately MAI-free OFDM transceiver, called the repetitively coded multicarrier CDMA (RCMC-CDMA), which uses the CDMA technique to separate different users so that every user can utilize the whole bandwidth and time slot simultaneously. We derive the capacity of the proposed system with multiple receive antennas, and then compare it to the capacity of the generalized MC-CDMA system. Furthermore, the proposal system is robust to timing mismatch between different users so that it is suitable for uplink transmission. Finally, we show that the system has a great potential to mitigate the carrier frequency offset (CFO) problem.
Joint MIMO channel tracking and symbol detection with EM algorithm and soft decoding

An expectation maximization (EM) algorithm for joint channel tracking and symbol detection in a multi-input multi-output (MIMO) time-varying frequency-selective fading environment is proposed in this research. Based on the 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. Two soft decoders are examined; namely, the BCJR algorithm and the soft sphere decoder. The performance of the proposed algorithm is evaluated via simulation and compared with that of Kalman filtering with hard decision feedback. It is demonstrated by numerical simulation that the proposed algorithm has robust performance in the presence of a severe channel model mismatch
A wavelet transform approach for hierarchical texture segmentation

An algorithm that uses both the tree-structured wavelet transform and a multiscale node linking technique for hierarchical texture segmentation is developed. The tree-structured wavelet transform, which offers a nonredundant representation, is able to zoom into dominant frequency channels containing significant information of textures and can be interpreted as the decomposition of a 2-D function with the wavelet packet basis function. Based on the tree-structure wavelet representation, a fuzzy clustering algorithm is then used to implement the multiscale node linking. Numerical experiments are given to demonstrate the performance of the new algorithm.<<ETX>>
A Modified Kalman Filtering Approach to On-Line Musical Beat Tracking

A modified Kalman filtering approach to on-line music beat tracking is proposed in this work. The proposed algorithm first detects onset signals from the acoustic waveform. Then, it tracks the beat and the tempo using the Kalman filter. Two techniques (i.e., observation smoothing and lock detection) are adopted to improve the tracking performance furthermore. It is shown by experimental results that the proposed modified Kalman filtering approach provides satisfactory beat tracking performance.
A Distortion-Based Approach to Privacy-Preserving Metering in Smart Grids

In this paper, we propose an efficient distortion-based privacy-preserving metering scheme that protects an individual customer's privacy and provides the complete power consumption distribution curve of a multitude of customers without privacy invasion. In the proposed scheme, a random noise is purposely introduced to distort customers' power consumption data at the smart meter so that data recovery becomes infeasible. Using the power consumption data and prior knowledge about added random noise, we develop an efficient algorithm for power consumption distribution reconstruction needed for power demand analysis and prediction. As a complete solution, our scheme also supports a privacy-preserving billing service. Using experimental results from real world single household power consumption data set and synthesized data of a large number of households, we demonstrate that the proposed scheme is robust against known attacks. Since it does not demand new facilities on existing smart grids, the proposed scheme offers a practical solution.
Code priority of multiuser OFDM systems in frequency asynchronous environments

An approximately MAI-free multiaccess OFDM transceiver, called the precoded multiuser (PMU-OFDM) OFDM system here, was proposed in (5). In this work, we extend results in (5), and propose a code priority scheme based on the even/odd Hadamard-Walsh code selection to enhance the performance of the PMU-OFDM system in a CFO environment. Based on the observation that different codewords have different contributions to multiaccess interference (MAI) that are correlated with the number of crossings of a particular Hadamard-Walsh codeword, we propose a code priority scheme by ranking codewords according to their contributions to MAI. Simulation results are given to demonstrate the advantages of the proposed priority.
Fragile speech watermarking for content integrity verification

Two fragile speech watermarking schemes for content integrity, i.e. exponential-scale odd/even modulation and linear additive watermarking, are proposed and compared with respect to their ability in detecting localized malicious alterations and tolerating content preserving operations. Both algorithms embed watermarks in the DFT magnitude domain by using a simplified masking model to achieve inaudibility. Statistical analysis is performed to identify the detection failure rate and the false detection rate of these two algorithms. These two schemes require no auxiliary authentication data to perform integrity verification, and could distinguish various content preserving operations from malicious tampering. The exponential-scale odd/even modulation is better in detecting localized content alteration while linear additive watermarking is more tolerant to low bit-rate speech coders such as the CELP coders.